R Consortium Wiki https://wiki.r-consortium.org/view/Main_Page MediaWiki 1.23.15 first-letter Media Special Talk User User talk R Consortium Wiki R Consortium Wiki talk File File talk MediaWiki MediaWiki talk Template Template talk Help Help talk Category Category talk Campaign Campaign talk 0 3 84 47 2018-04-12T15:46:51Z MarkHornick 5 wikitext text/x-wiki '''Working Group: Code Coverage Tool for R''' Code coverage helps to ensure greater software quality by reporting how thoroughly test suites cover the various code paths. Having a tool that supports the breadth of the R language across multiple platforms, and that is used by R package developers and R core teams, helps to improve software quality for the R Community. While a few code coverage tools exist for R, this Oracle-proposed ISC project aims to provide an enhanced tool that addresses feature and platform limitations of existing tools via an ISC-established working group. It also aims to promote the use of code coverage more systematically within the R ecosystem. The Code Coverage group released a revised covr package over the summer 2017 ([https://www.r-consortium.org/blog/2017/06/28/code-coverage-tool-for-r-working-group-achieves-first-release]). Github project: https://github.com/jimhester/covr/issues === Working Group Members === * Shivank Agrawal, Oracle * Chris Campbell, Mango Solutions * Santosh Chaudhari, Oracle * Karl Forner, Quartz Bio * Jim Hester, RStudio * Mark Hornick, Oracle – Group Leader * Chen Liang, Oracle * Willem Ligtenberg, Open Analytics * Vlad Sharanhovich, Oracle * Tobias Verbeke, Open Analytics * Qin Wang, Oracle * Hadley Wickham, RStudio – ISC Sponsor === Status as of August 10, 2017 === * Branch coverage with profile stats – Deferred * Define canonical performance benchmark suite – Done * #144 store test results as well as coverage – Deferred * #134 ICC compatibility – Done * Make code coverage use more pervasive in R community – in progress * Correct behavior for parallel code execution – DONE * Resolve package dependency license issues – DONE * #174 R6 methods are not covered - DONE * #117 covr with a local jenkins builder – DONE * #99 Shiny Source Tab Indicate the Filename – DONE * Observations testing ORE with covr – DONE === Meeting History === * November 30, 2017 * November 8, 2017 * September 14, 2017 * August 24, 2017 * August 10, 2017 * July 27, 2017 * June 15, 2017 * March 9, 2017 * February 16, 2017 * January 26, 2017 * January 5, 2017 * December 15, 2016 * October 20, 2016 * October 4, 2016 * September, 22, 2016 * September 8, 2016 * August 18, 2016 * July 28, 2016 * July 7, 2016 * June 16, 2016 * June 2, 2016 * May 24, 2016 * May 15, 2016 6cdc9fed1a4c8035f357f3da745a54b43b4d5a39 47 14 2016-10-18T20:02:05Z MarkHornick 5 wikitext text/x-wiki '''Working Group: Code Coverage Tool for R''' Code coverage helps to ensure greater software quality by reporting how thoroughly test suites cover the various code paths. Having a tool that supports the breadth of the R language across multiple platforms, and that is used by R package developers and R core teams, helps to improve software quality for the R Community. While a few code coverage tools exist for R, this Oracle-proposed ISC project aims to provide an enhanced tool that addresses feature and platform limitations of existing tools via an ISC-established working group. It also aims to promote the use of code coverage more systematically within the R ecosystem. Github project: https://github.com/jimhester/covr/issues === Working Group Members === * Shivank Agrawal, Oracle * Chris Campbell, Mango Solutions * Santosh Chaudhari, Oracle * Karl Forner, Quartz Bio * Jim Hester, RStudio * Mark Hornick, Oracle – Group Leader * Chen Liang, Oracle * Willem Ligtenberg, Open Analytics * Andy Nicholls, Mango Solutions * Vlad Sharanhovich, Oracle * Tobias Verbeke, Open Analytics * Qin Wang, Oracle * Hadley Wickham, RStudio – ISC Sponsor === Status as of October 4, 2016 === * Branch coverage with profile stats – in progress * Define canonical performance benchmark suite – in progress * #144 store test results as well as coverage – in progress * #134 ICC compatibility – TESTS COMPLETE * Make code coverage use more pervasive in R community – in progress * Correct behavior for parallel code execution – DONE * Resolve package dependency license issues – DONE * #174 R6 methods are not covered - DONE * #117 covr with a local jenkins builder – DONE * #99 Shiny Source Tab Indicate the Filename – DONE * Observations testing ORE with covr – DONE === Meeting History === * October 4, 2016 * September, 22, 2016 * September 8, 2016 * August 18, 2016 * July 28, 2016 * July 7, 2016 * June 16, 2016 * June 2, 2016 * May 24, 2016 * May 15, 2016 d09485ffb3d761a3ece1afceca526e6a7268bfd8 14 8 2016-06-17T16:11:52Z MarkHornick 5 wikitext text/x-wiki '''Working Group: Code Coverage Tool for R''' Code coverage helps to ensure greater software quality by reporting how thoroughly test suites cover the various code paths. Having a tool that supports the breadth of the R language across multiple platforms, and that is used by R package developers and R core teams, helps to improve software quality for the R Community. While a few code coverage tools exist for R, this Oracle-proposed ISC project aims to provide an enhanced tool that addresses feature and platform limitations of existing tools via an ISC-established working group. It also aims to promote the use of code coverage more systematically within the R ecosystem. ISC Proposal Document Meeting Minutes === Working Group Members === * Shivank Agrawal, Oracle * Chris Campbell, Mango Solutions * Santosh Chaudhari, Oracle * Karl Forner, Quartz Bio * Jim Hester, RStudio * Mark Hornick, Oracle – Group Leader * Chen Liang, Oracle * Willem Ligtenberg, Open Analytics * Andy Nicholls, Mango Solutions * Vlad Sharanhovich, Oracle * Tobias Verbeke, Open Analytics * Qin Wang, Oracle * Hadley Wickham, RStudio – ISC Sponsor 5edf4b62d84464143580ff67af5050b3a891cb69 8 7 2016-05-13T18:20:54Z MarkHornick 5 wikitext text/x-wiki '''Working Group: Code Coverage Tool for R''' Code coverage helps to ensure greater software quality by reporting how thoroughly test suites cover the various code paths. Having a tool that supports the breadth of the R language across multiple platforms, and that is used by R package developers and R core teams, helps to improve software quality for the R Community. While a few code coverage tools exist for R, this Oracle-proposed ISC project aims to provide an enhanced tool that addresses feature and platform limitations of existing tools via an ISC-established working group. It also aims to promote the use of code coverage more systematically within the R ecosystem. ISC Proposal Document Meeting Minutes 857f23d392800954460754645868233b620a9738 7 6 2016-05-13T18:20:37Z MarkHornick 5 wikitext text/x-wiki '''Working Group: Code Coverage Tool for R''' Code coverage helps to ensure greater software quality by reporting how thoroughly test suites cover the various code paths. Having a tool that supports the breadth of the R language across multiple platforms, and that is used by R package developers and R core teams, helps to improve software quality for the R Community. While a few code coverage tools exist for R, this Oracle-proposed ISC project aims to provide an enhanced tool that addresses feature and platform limitations of existing tools via an ISC-established working group. It also aims to promote the use of code coverage more systematically within the R ecosystem. ISC Proposal Document Meeting Minutes 425274f52d0b101c72f7a63c3916bddddd553a52 6 2016-03-23T22:00:31Z MarkHornick 5 Created page with "'''Working Group: Code Coverage Tool for R''' Code coverage helps to ensure greater software quality by reporting how thoroughly test suites cover the various code paths. Hav..." wikitext text/x-wiki '''Working Group: Code Coverage Tool for R''' Code coverage helps to ensure greater software quality by reporting how thoroughly test suites cover the various code paths. Having a tool that supports the breadth of the R language across multiple platforms, and that is used by R package developers and R core teams, helps to improve software quality for the R Community. While a few code coverage tools exist for R, this Oracle-proposed ISC project aims to provide an enhanced tool that addresses feature and platform limitations of existing tools via an ISC-established working group. It also aims to promote the use of code coverage more systematically within the R ecosystem. c01fb120da6b858ef7a6d264fcda7eb2b50783a1 0 19 73 2017-05-01T17:59:37Z MichaelLawrence 9 Created page with "= Distributed Computing Working Group Progress Report: 2016 = == Introduction == Data sizes continue to increase, while single core performance has stagnated. We scale comp..." wikitext text/x-wiki = Distributed Computing Working Group Progress Report: 2016 = == Introduction == Data sizes continue to increase, while single core performance has stagnated. We scale computations by leveraging multiple cores and machines. Large datasets are expensive to replicate, so we minimize data movement by moving the computation to the data. Many systems, such as Hadoop, Spark, and massively parallel processing (MPP) databases, have emerged to support these strategies, and each exposes its own unique interface, with little standardization. Developing and executing an algorithm in the distributed context is a complex task that requires specific knowledge of and dependency on the system storing the data. It is also a task orthogonal to the primary role of a data scientist or statistician: extracting knowledge from data. The task thus falls to the data analysis environment, which should mask the complexity behind a familiar interface, maintaining user productivity. However, it is not always feasible to automatically determine the optimal strategy for a given problem, so user input is often beneficial. The environment should only abstract the details to the extent deemed appropriate by the user. R needs a standardized, layered and idiomatic abstraction for computing on distributed data structures. R has many packages that provide parallelism constructs as well as bridges to distributed systems such as Hadoop. Unfortunately, each interface has its own syntax, parallelism techniques, and supported platform(s). As a consequence, contributors are forced to learn multiple idiosyncratic interfaces, and to restrict each implementation to a particular interface, thus limiting the applicability and adoption of their software and hampering interoperability. The idea of a unified interface stemmed from a cross-industry workshop organized at HP Labs in early 2015. The workshop was attended by different companies, universities, and R-core members. Immediately after the workshop, Indrajit Roy, Edward Ma, and Michael Lawrence began designing an abstraction that later became known as the CRAN package ddR (Distributed Data in R)[1]. It declares a unified API for distributed computing in R and ensures that R programs written using the API are portable across different systems, such as Distributed R, Spark, etc. The ddR package has completed its initial phase of development; the first release is now on CRAN. Three ddR machine-learning algorithms are also on CRAN, randomForest.ddR, glm.ddR, and kmeans.ddR. Two reference backends for ddR have been completed, one for R’s parallel package, and one for HP Distributed R. Example code and scripts to run algorithms and code on both of these backends are available in our public repository at https://github.com/vertica/ddR. The overarching goal of the ddR project was for it to be a starting point in a collaborative effort, ultimately leading to a standard API for working with distributed data in R. We decided that it was natural for the R Consortium to sponsor the collaboration, as it should involve both industry and R-core members. To this end, we established the R Consortium Working Group on Distributed Computing, with a planned duration of a single year and the following aims: # Agree on the goal of the group, i.e., we should have a unifying framework for distributed computing. Define success metric. # Brainstorm on what primitives should be included in the API. We can use ddR’s API of distributed data-structures and dmapply as the starting proposal. Understand relationship with existing packages such as parallel, foreach, etc. # Explore how ddR like interface will interact with databases. Are there connections or redundancies with dplyr and multiplyr? # Decide on a reference implementation for the API. # Decide on whether we should also implement a few ecosystem packages, e.g., distributed algorithms written using the API. We declared the following milestones: # Mid-year milestone: Finalize API. Decide who all will help with developing the top-level implementation and backends. # End-year milestone: Summary report and reference implementation. Socialize the final package. This report outlines the progress we have made on the above goals and milestones, and how we plan to continue progress in the second half of the working group term. == Results and Current Status == The working group has achieved the first goal by agreeing that we should aim for a unifying distributed computing abstraction, and we have treated ddR as an informal API proposal. We have discussed many of the issues related to the second goal, deciding which primitives should be part of the API. We aim for the API to support three shapes of data --- lists, arrays and data frames --- and to enable the loading and basic manipulation of distributed data, including multiple modes of functional iteration (e.g., apply() operations). We aim to preserve consistency with base R data structures and functions, so as to provide a simple path for users to port computations to distributed systems. The ddR constructs permit a user to express a wide variety of applications, including machine-learning algorithms, that will run on different backends. We have successfully implemented distributed versions of algorithms such as K-means, Regression, Random Forest, and PageRank using the ddR API. Some of these ddR algorithms are now available on CRAN. In addition, the package provides several generic definitions of common operators (such as colSums) that can be invoked on distributed objects residing in the supporting backends. Each custom ddR backend is encapsulated in its own driver package. In the conventional style of functional OOP, the driver registers methods for generics declared by the backend API, such that ddR can dispatch the backend-specific instructions by only calling the generics. The working group explored potential new backends with the aim of broadening the applicability of the ddR interface. We hosted presentations from external speakers on Spark and TensorFlow, and also considered a generic SQL backend. The discussion focused on Spark integration, and the R Consortium-funded intern Clark Fitzgerald took on the task of developing a prototype Spark backend. The development of the Spark backend encountered some obstacles, including the immaturity of Spark and its R interfaces. Development is currently paused, as we await additional funding. During the monthly meetings, the working group deliberated on different design improvements for ddR itself. We list two key topics that were discussed. First, Michael Kane and Bryan Lewis argued for a lower level API that directly operates on chunks of data. While ddR supports chunk-wise data processing, via a combination of dmapply() and parts(), its focus on distributed data structures means that the chunk-based processing is exposed as the manipulation of these data structures. Second, Clark Fitzgerald proposed restructuring the ddR code into two layers that includes chunk-wise processing while retaining the emphasis on distributed data structures[2]. The lower level API, which will interface with backends, will use a Map() like primitive to evaluate functions on chunks of data, while the higher level ddR API will expose distributed data structures, dmapply, and other convenience functions. This refactoring would facilitate the implementation of additional backends. == Discussion and Future Plans == The R Consortium-funded working group and internship has helped us start a conversation on distributed computing APIs for R. The ddR CRAN package is a concrete outcome of this working group, and serves as a platform for exploring APIs and their integration with different backends. While ddR is still maturing, we have arrived at a consensus for how we should improve and finalize the ddR API. As part of our goal for a reference implementation, we aim to develop one or more prototype backends that will make the ddR interface useful in practice. A good candidate backend is any open-source system that is effective at R use cases and has strong community support. Spark remains a viable candidate, and we also aim to further explore TensorFlow. We plan for a second intern to perform three tasks: (1) refactor the ddR API to a more final form, (2) compare Spark and TensorFlow in detail, with an eye towards the feasibility of implementing a useful backend, and (3) implement a prototype backend based on Spark or TensorFlow, depending on the recommendation of the working group. By the conclusion of the working group, it will have produced: * A stable version of the ddR package and at least one practical backend, released on CRAN, * A list of requirements that are relevant and of interest to the community but have not yet been met by ddR, including alternative implementations that remain independent, * A list of topics that the group believes worthy of further investigation. [1] http://h30507.www3.hp.com/t5/Behind-the-scenes-Labs/Enhancing-R-for-Distributed-Computing/ba-p/6795535#.VjE1K7erQQj [2] Clark Fitzgerald. https://github.com/vertica/ddR/wiki/Design b2d84c4d967e694ba77d843d8d13965aaa43d746 0 12 80 78 2017-05-12T02:53:20Z Indrajit roy 14 wikitext text/x-wiki == Goals and Purpose == The Distributed Computing Working Group will endorse the design of a common abstraction for distributed data structures in R. We aim to have at least one open-source implementation, as well as a SQL implementation, released within a year of forming the group. == Members == * '''Michael Lawrence''' (Genentech) * '''Indrajit Roy''' (Google) * ''Joe Rickert'' (ISC liason, RStudio) * Bernd Bischl (LMU) * Matt Dowle (H2O) * Mario Inchiosa (Microsoft) * Michael Kane (Yale) * Javier Luraschi (RStudio) * Edward Ma (HP Enterprise) * Luke Tierney (University of Iowa) * Simon Urbanek (AT&T) * Bryan Lewis (Paradigm4) * Hossein Falaki (databricks) == Reports == [[Distributed Computing Working Group Progress Report 2016|2016 Progress Report]] == Milestones == === Achieved === * Adopt ddR as a prototype for a standard API for distributed computing in R === 2016 Internship === Clark Fitzgerald, a PhD student in the UC Davis Statistics department, worked on ddR and Spark integration. * Wrote [https://github.com/clarkfitzg/sparklite sparklite] and [https://github.com/clarkfitzg/rddlist rddlist] as minimal proof-of-concept R packages to connect and store general data on Spark. [https://docs.google.com/presentation/d/1WfUQ2ockNku90GWMXonEhUEcVOWcgBmWwt5uYSSBYPY/edit?usp=sharing slides] * [https://issues.apache.org/jira/browse/SPARK-16785 Patched SparkR] to allow user defined functions returning binary columns. This allows implementation of different data structures in SparkR. * Updated [https://github.com/vertica/ddR/wiki/Design design documents] with suggested changes to DDR's internal design and object oriented model. * Improved [https://github.com/vertica/ddR/pull/15 testing and ddR internals]. === Outstanding === * Agree on a final standard API for distributed computing in R * Implement at least one scalable backend based on an open-source technology like Spark, SQL, etc == Open Questions == * How can we address the needs of both the end user data scientists and the algorithm implementers? * How should we share data between R and a system like Spark? * Is there any way to unify SparkR and sparklyr? * Could we use the abstractions of tensorflow to partially or fully integrate with platforms like Spark? == Minutes == === 05/11/2017 === * Hernik presented his work on “Futures in R: Atomic Building Blocks for Asynchronous Evaluation” ** Future is an abstraction for a value that will be available later ** Futures are in one of the two states— resolved or unresolved ** Syntax for explicit future: f<- future(expo) v<-value(f) ** There are many ways to resolve futures. E.g., sequential, multicore, multisession, etc. ** Aim is to make the future package “write once, run anywhere”. It works on all platforms. ** In future api, the developer decides what to parallelize and the user decides how to ** future.Batchjobs a future API on top of BatchJobs (map-reduce API for HPC schedulers) ** Hernik presented an example of using DNA-sequence files with the future API. ** Futures can be nested. E.g., one can create a future in an outer loop and create more futures within the inner loop. ** The plan() function can be used with futures to decide whether the futures are run in parallel or sequentially. ** The doFuture() package is a foreach adaptor. It allows foreach to utilize HPC clusters. Slides are available here: [[File:BengtssonH 20170511-future,RConsortium,flat.pdf|thumb|Futures in R]] === 03/09/2017 === * Talk by Brian Lewis ** Has created a Github page with notes on Singularity. ** Singularity is a container technology for HPC applications ** No daemon. Minimum virtualization to get application running. Light weight and has very low overheads. ** Used widely in supercomputers ** All distributed computing platforms even with R skins are difficult to use. ** Containers make it much easier to abstract away the long tail of software dependencies and focus on R ** Demonstrated an example of using Singularity with Tensorflow ** Tried MPI and dopar on the 1000Genome data ** The program parses the variant data and stores chunks as files. Then ran principal components on each file. ** Overload matrix operations to use foreach/MPI underneath. ** Overall: Use existing R operators and overloading them with the appropriate backend. ** Will spend time working on Tensorflow, e.g., take a number of algorithms such as PCA and write them on top of Tensorflow using existing R primitives. === 12/08/2016 === * Yuan Tang from Uptake was the presenter ** Michael and Indrajit will write a status report for the working group sometime in December or January ** Yuan gave an overview of TensorFlow ** JJ, Dirk and Yuan are working on R layer for TensorFlow ** TensorFlow is a platform for machine learning as well as other computations (even math proofs). ** It is GPU optimized and distributed. ** It is used in search, speech recognition, Google photos, etc. ** TensorFlow computations are directed graphs. odes are operations and edges are tensors. ** A lot of array, matrix, etc. operations are available ** Backend is mostly C++. Python front end exists. ** TensorFlow R is based on the python fronted ** In multi-device setting, TensorFlow figures out which devices to use and manages communication between devices. ** Computations are fault tolerant ** Yuan has previously worked on Scikit Flow which is now TF.Learn. It’s a easy transition for Scikit learn users. ** Yuan gave a brief overview of the python interface ** TensorFlow in R handles conversion between R and Python. Syntax is very similar to python API ** Future work: Adding more examples and tutorials, integration with Kubernetes/Marathon like framework. ** During the Q/A there were questions related to whether R kernels can be supported in TensorFlow, and whether R dataframes are a natural wrapper for TensorFlow objects. === 11/10/2016 === * SparkR slides were presented by Hossein Faliki and Shivaram from Databricks and UC Berkeley: ** SparkR was a prototype from AMPLab (2014). Initially it had the RDD API and was similar to PySpark API ** In 2015, the merge with upstream Spark, the decision was made to integrate with the Dataframe API, and hide the RDD API ** In 2016 more MLLib algorithms have been integrated and new APIs have been added. A CRAN package will be released soon ** Original SparkR architecture runs R on the master that communicates with the JVM processes in the driver. the driver sends commands to the worker JVM processes, and executes them as scala/java statements. ** The system can read distributed data inside the JVM from different sources such as S3, HDFS, etc. ** The driver has a socket based connection between SparkR and the RBackend. RBackend runs on the JVM, deserializes the R code, and converts the R statements into Java calls. ** collect() and createDataFrame() are used to move data between R and JVM processes. createDataFrame will convert your local R data into a JVM based distributed data frame. ** The API has IO, Caching, MLLib, and SQL related commands ** Since Spark 2.0, we can run R processes inside the JVM worker processes. There is no need to keep long running R processes. ** There are 3 UDF functions (1) lapply, runs function on different value of a list (2) dapply, runs function on each partition of a data frame. You have to careful about how data is partitioned, and (3) gapply, performs a grouping on different column names and then runs the function on each group. ** The new CRAN package install.spark() will automatically download and install Spark. Automated CRAN checks have been added to every commit to the code. Should be available with Spark 2.1.0 * Q/A ** Currently trying to get zero copy dataframe between python and Spark. Spark 2.0 has an off heap manager that uses Arrow. Once this feature is tested on the Python API, the next step will be integration R. ** Spark dataframes gain from plan optimizations. It is not SparkR specific. R UDFs are still treated as black boxes by the optimizer ** Spark doesn't directly support matrixes. There is no immediate intent to do so either. One can store an array or vector as a single column of a Spark dataframe. === 10/13/2016 === ''Detailed minutes were not taken for this meeting'' * Mario Inchiosa: Microsoft's perspective on distributed computing with R ** Microsoft R Server: abstractions and algorithms for distributed computation on top of open-source R ** Desired features of a distributed API like ddR: *** Supports PEMA (initialize, processData, updateResults, processResults) *** Cross-platform *** Fast runtime *** Supports algorithm writer and data scientist *** Comes with a comprehensive set of algorithms *** Easy deployment ** ddR is making good progress but does not yet meet those requirements * Indrajit: ddR progress report and next steps ** Recap of Clark's internship ** Next step: implement some of Clark's design suggestions: https://github.com/vertica/ddR/wiki/Design ** Spark integration will be based on sparklyr ** Should we limit Spark interaction to the DataFrame API or directly interact with RDDs? *** Consensus: will likely need flexibility of RDDs to implement everything we need, e.g., arrays and lists ** Clark and Javier raised concerns about the scalability of sharing data between R and Spark *** Michael: Spark is a platform in its own right, so interoperability is important, should figure something out *** Bryan Lewis: Why not use tensor abstraction from tensorflow? Spark supports tensorflow and an R interface is already in the works. ** Michael raised the issue of additional funding from the R Consortium to continue Clark's work *** Joe Rickert suggested that the working group develop one or more white papers summarizing the findings of the working group for presentation to the Infrastructure Steering Committee. *** Consensus was in favor of this, and several pointed out that the progress so far has been worthwhile, despite not meeting the specific goals laid out in the proposal. * Michael: do we want to invite some external speakers, one per meeting, from groups like databricks, tensorflow, etc? ** Consensus was in favor. === 9/8/2016 === ''Detailed minutes were not taken for this meeting'' * Clark Fitzgerald: internship report ** Developed two packages for low-level Spark integration: rddlist, sparklite ** Patched a bug in Spark ** ddR needs refactoring before Spark integration is feasible: *** dlist, dframe, and darray should be formal classes. *** Partitions of data should be represented by a distributed list abstraction, and most functions (e.g., dmapply) should be implemented on top of that list. * Javier: sparklyr update ** Preparing for CRAN release ** Mario: what happened to sparkapi? *** Javier: sparkapi has been merged into sparklyr in order to avoid overhead of maintaining two packages. ddR can do everything it needs with sparklyr. * Luke Tierney: Update on the low-level vector abstraction, which might support interfaces like ddR and sparklyr. ** Overall approach seems feasible, but still working out a few details. ** Will land in a branch soon. * Bernd Bischl: update on the batchtools package ** Successor to BatchJobs based on in-memory database === 8/11/2016 === ''Meeting was canceled due to lack of availability.'' === 7/14/2016 === * Introduced Clark who is the intern funded by R Consortium. Clark is a graduate student from UC Davis. He will work on ddR integration with Spark and improving the core ddR API as well such as adding a distributed apply() for matrices, split function, etc. * Bernd: Can I play around with ddR now? What backend should I use? How robust is the code? ** Clark: It's in good enough shape to be played around with. We will continue to improve it. Hopefully the spark integration will be done before the end of my internship in September. * Q: Is anyone working on using ddR to make ML scale better. ** Indrajit: We have kmeans, glm, etc. already in CRAN. ** Michael Kane: We are working on glmnet and other packages related to algorithm development. * Javier gave a demo of sparklyr and sparkapi. ** Motivation for the pacakage: The SparkR package overrides the dplyr interface. This is an issue for RStudio. SparkR is not a CRAN package which makes it difficult to add changes. dplyr is the most popular tool by RStudio and is broken on SparkR. ** Sparklyr provides a dplyr interface. It will also support ML like interfaces, such as consuming a ML model. ** Sparklyr does not currently support any distributed computing features. Instead we can recommend ddR as the distributed computing framework on top of sparkapi. We will put the code in CRAN in a couple of weeks. ** Simon: Can you talk more about the wrapper/low level API to work with Spark? *** Javier: The package underneath the cover is called "sparkapi" it is to be used by pacakge builders. "spark_context()" and "invoke()" are the functionality to call scala methods. It does not you to currently run R user defined functions. I am currently working on enabling that feature. Depending upon the interest in using ddR with sparkapi, I can spend more time to make sparkapi feature rich. ** Indrajit: What versions of Spark are supported *** Javier: Anything after 1.6 ** Bernd: How do you export data? *** Javier: We are using all the code from SparkR. So everything in SparkR should continue to work. We don't need to change SparkR. We just need to maintain compatibility. ** Bernd: What happens when the RDDs are very large? *** Javier: Spark will spill on disk. * Michael Kane: Presented examples that he implemented on ddR. ** Talked about how the different distributed packages compare to each other in terms of functionality. ** Michael K. looked at glm and truncated SVD on ddR. Was able to implement irls on ddR by implementing two distributed functions such as "cross". In truncated SVD only needed to overload two different distributed multiplications. ** Ran these algorithms on the 1000 genome dataset. ** Overall liked ddR since it was easy to implement the algorithms in the package. ** New ideas: *** Trying to separate the data layer from the execution layer *** Create an API that works on "chunks" (which is similar to the "parts" API in ddR). Would like to add these APIs to ddR. *** Indrajit: You should be able to get some of the chunk like features by using parts and dmapply. E.g., you can call dmapply to read 10 different files, which correspond 10 chunks now. These are however wrapped as a darray or dframe. But you can continue to work on the individual chunks by using parts(i). === 6/2/2016 === * Round table introduction * (Michael) Goals for the group: ** Make a common abstraction/interfaces to make it easier to work with distributed data and R ** Unify the interface ** Working group will run for a year. Get an API defined, get at least one open source reference implementations ** not everyone needs to work hands on. We will create smaller groups to focus on those aspects. ** We tried to get a diverse group of participants * Logistics: meet monthly, focus groups may meet more often * R Consoritum may be able to figure ways to fund smaller projects that come out of the working group * Michael Kane: Should we start with an inventory of what is available and people are using? ** Michael Lawrence: Yes, we should find the collection of tools as well as the use cases that are common. ** Joe: I will figure out a wiki space. * Javier: Who are the end users? Simon: Common layer needed to get algorithms working. We started from algos and tried to find the minimal common api. One of the goals is to make sure everyone is on the same page and not trying to create his/her own custom interface. * Javier: Should we try to get people with more algo expertise? * Joe: Simon do you have a stack diagram? * Simon: Can we get R Consortium to help write things up and draw things? * Next meeting: Javier is going to present SparkR next time. 8a51c2958e685a43b6564702a0887d8148865fb0 78 77 2017-05-12T02:39:26Z Indrajit roy 14 wikitext text/x-wiki == Goals and Purpose == The Distributed Computing Working Group will endorse the design of a common abstraction for distributed data structures in R. We aim to have at least one open-source implementation, as well as a SQL implementation, released within a year of forming the group. == Members == * '''Michael Lawrence''' (Genentech) * '''Indrajit Roy''' (Google) * ''Joe Rickert'' (ISC liason, RStudio) * Bernd Bischl (LMU) * Matt Dowle (H2O) * Mario Inchiosa (Microsoft) * Michael Kane (Yale) * Javier Luraschi (RStudio) * Edward Ma (HP Enterprise) * Luke Tierney (University of Iowa) * Simon Urbanek (AT&T) * Bryan Lewis (Paradigm4) * Hossein Falaki (databricks) == Reports == [[Distributed Computing Working Group Progress Report 2016|2016 Progress Report]] == Milestones == === Achieved === * Adopt ddR as a prototype for a standard API for distributed computing in R === 2016 Internship === Clark Fitzgerald, a PhD student in the UC Davis Statistics department, worked on ddR and Spark integration. * Wrote [https://github.com/clarkfitzg/sparklite sparklite] and [https://github.com/clarkfitzg/rddlist rddlist] as minimal proof-of-concept R packages to connect and store general data on Spark. [https://docs.google.com/presentation/d/1WfUQ2ockNku90GWMXonEhUEcVOWcgBmWwt5uYSSBYPY/edit?usp=sharing slides] * [https://issues.apache.org/jira/browse/SPARK-16785 Patched SparkR] to allow user defined functions returning binary columns. This allows implementation of different data structures in SparkR. * Updated [https://github.com/vertica/ddR/wiki/Design design documents] with suggested changes to DDR's internal design and object oriented model. * Improved [https://github.com/vertica/ddR/pull/15 testing and ddR internals]. === Outstanding === * Agree on a final standard API for distributed computing in R * Implement at least one scalable backend based on an open-source technology like Spark, SQL, etc == Open Questions == * How can we address the needs of both the end user data scientists and the algorithm implementers? * How should we share data between R and a system like Spark? * Is there any way to unify SparkR and sparklyr? * Could we use the abstractions of tensorflow to partially or fully integrate with platforms like Spark? == Minutes == === 05/11/2017 === * Hernik presented his work on “Futures in R: Atomic Building Blocks for Asynchronous Evaluation” ** Future is an abstraction for a value that will be available later ** Futures are in one of the two states— resolved or unresolved ** Syntax for explicit future: f<- future(expo) v<-value(f) ** There are many ways to resolve futures. E.g., sequential, multicore, multisession, etc. ** Aim is to make the future package “write once, run anywhere”. It works on all platforms. ** In future api, the developer decides what to parallelize and the user decides how to ** future.Batchjobs a future API on top of BatchJobs (map-reduce API for HPC schedulers) ** Hernik presented an example of using DNA-sequence files with the future API. ** Futures can be nested. E.g., one can create a future in an outer loop and create more futures within the inner loop. ** The plan() function can be used with futures to decide whether the futures are run in parallel or sequentially. ** The doFuture() package is a foreach adaptor. It allows foreach to utilize HPC clusters. === 03/09/2017 === * Talk by Brian Lewis ** Has created a Github page with notes on Singularity. ** Singularity is a container technology for HPC applications ** No daemon. Minimum virtualization to get application running. Light weight and has very low overheads. ** Used widely in supercomputers ** All distributed computing platforms even with R skins are difficult to use. ** Containers make it much easier to abstract away the long tail of software dependencies and focus on R ** Demonstrated an example of using Singularity with Tensorflow ** Tried MPI and dopar on the 1000Genome data ** The program parses the variant data and stores chunks as files. Then ran principal components on each file. ** Overload matrix operations to use foreach/MPI underneath. ** Overall: Use existing R operators and overloading them with the appropriate backend. ** Will spend time working on Tensorflow, e.g., take a number of algorithms such as PCA and write them on top of Tensorflow using existing R primitives. === 12/08/2016 === * Yuan Tang from Uptake was the presenter ** Michael and Indrajit will write a status report for the working group sometime in December or January ** Yuan gave an overview of TensorFlow ** JJ, Dirk and Yuan are working on R layer for TensorFlow ** TensorFlow is a platform for machine learning as well as other computations (even math proofs). ** It is GPU optimized and distributed. ** It is used in search, speech recognition, Google photos, etc. ** TensorFlow computations are directed graphs. odes are operations and edges are tensors. ** A lot of array, matrix, etc. operations are available ** Backend is mostly C++. Python front end exists. ** TensorFlow R is based on the python fronted ** In multi-device setting, TensorFlow figures out which devices to use and manages communication between devices. ** Computations are fault tolerant ** Yuan has previously worked on Scikit Flow which is now TF.Learn. It’s a easy transition for Scikit learn users. ** Yuan gave a brief overview of the python interface ** TensorFlow in R handles conversion between R and Python. Syntax is very similar to python API ** Future work: Adding more examples and tutorials, integration with Kubernetes/Marathon like framework. ** During the Q/A there were questions related to whether R kernels can be supported in TensorFlow, and whether R dataframes are a natural wrapper for TensorFlow objects. === 11/10/2016 === * SparkR slides were presented by Hossein Faliki and Shivaram from Databricks and UC Berkeley: ** SparkR was a prototype from AMPLab (2014). Initially it had the RDD API and was similar to PySpark API ** In 2015, the merge with upstream Spark, the decision was made to integrate with the Dataframe API, and hide the RDD API ** In 2016 more MLLib algorithms have been integrated and new APIs have been added. A CRAN package will be released soon ** Original SparkR architecture runs R on the master that communicates with the JVM processes in the driver. the driver sends commands to the worker JVM processes, and executes them as scala/java statements. ** The system can read distributed data inside the JVM from different sources such as S3, HDFS, etc. ** The driver has a socket based connection between SparkR and the RBackend. RBackend runs on the JVM, deserializes the R code, and converts the R statements into Java calls. ** collect() and createDataFrame() are used to move data between R and JVM processes. createDataFrame will convert your local R data into a JVM based distributed data frame. ** The API has IO, Caching, MLLib, and SQL related commands ** Since Spark 2.0, we can run R processes inside the JVM worker processes. There is no need to keep long running R processes. ** There are 3 UDF functions (1) lapply, runs function on different value of a list (2) dapply, runs function on each partition of a data frame. You have to careful about how data is partitioned, and (3) gapply, performs a grouping on different column names and then runs the function on each group. ** The new CRAN package install.spark() will automatically download and install Spark. Automated CRAN checks have been added to every commit to the code. Should be available with Spark 2.1.0 * Q/A ** Currently trying to get zero copy dataframe between python and Spark. Spark 2.0 has an off heap manager that uses Arrow. Once this feature is tested on the Python API, the next step will be integration R. ** Spark dataframes gain from plan optimizations. It is not SparkR specific. R UDFs are still treated as black boxes by the optimizer ** Spark doesn't directly support matrixes. There is no immediate intent to do so either. One can store an array or vector as a single column of a Spark dataframe. === 10/13/2016 === ''Detailed minutes were not taken for this meeting'' * Mario Inchiosa: Microsoft's perspective on distributed computing with R ** Microsoft R Server: abstractions and algorithms for distributed computation on top of open-source R ** Desired features of a distributed API like ddR: *** Supports PEMA (initialize, processData, updateResults, processResults) *** Cross-platform *** Fast runtime *** Supports algorithm writer and data scientist *** Comes with a comprehensive set of algorithms *** Easy deployment ** ddR is making good progress but does not yet meet those requirements * Indrajit: ddR progress report and next steps ** Recap of Clark's internship ** Next step: implement some of Clark's design suggestions: https://github.com/vertica/ddR/wiki/Design ** Spark integration will be based on sparklyr ** Should we limit Spark interaction to the DataFrame API or directly interact with RDDs? *** Consensus: will likely need flexibility of RDDs to implement everything we need, e.g., arrays and lists ** Clark and Javier raised concerns about the scalability of sharing data between R and Spark *** Michael: Spark is a platform in its own right, so interoperability is important, should figure something out *** Bryan Lewis: Why not use tensor abstraction from tensorflow? Spark supports tensorflow and an R interface is already in the works. ** Michael raised the issue of additional funding from the R Consortium to continue Clark's work *** Joe Rickert suggested that the working group develop one or more white papers summarizing the findings of the working group for presentation to the Infrastructure Steering Committee. *** Consensus was in favor of this, and several pointed out that the progress so far has been worthwhile, despite not meeting the specific goals laid out in the proposal. * Michael: do we want to invite some external speakers, one per meeting, from groups like databricks, tensorflow, etc? ** Consensus was in favor. === 9/8/2016 === ''Detailed minutes were not taken for this meeting'' * Clark Fitzgerald: internship report ** Developed two packages for low-level Spark integration: rddlist, sparklite ** Patched a bug in Spark ** ddR needs refactoring before Spark integration is feasible: *** dlist, dframe, and darray should be formal classes. *** Partitions of data should be represented by a distributed list abstraction, and most functions (e.g., dmapply) should be implemented on top of that list. * Javier: sparklyr update ** Preparing for CRAN release ** Mario: what happened to sparkapi? *** Javier: sparkapi has been merged into sparklyr in order to avoid overhead of maintaining two packages. ddR can do everything it needs with sparklyr. * Luke Tierney: Update on the low-level vector abstraction, which might support interfaces like ddR and sparklyr. ** Overall approach seems feasible, but still working out a few details. ** Will land in a branch soon. * Bernd Bischl: update on the batchtools package ** Successor to BatchJobs based on in-memory database === 8/11/2016 === ''Meeting was canceled due to lack of availability.'' === 7/14/2016 === * Introduced Clark who is the intern funded by R Consortium. Clark is a graduate student from UC Davis. He will work on ddR integration with Spark and improving the core ddR API as well such as adding a distributed apply() for matrices, split function, etc. * Bernd: Can I play around with ddR now? What backend should I use? How robust is the code? ** Clark: It's in good enough shape to be played around with. We will continue to improve it. Hopefully the spark integration will be done before the end of my internship in September. * Q: Is anyone working on using ddR to make ML scale better. ** Indrajit: We have kmeans, glm, etc. already in CRAN. ** Michael Kane: We are working on glmnet and other packages related to algorithm development. * Javier gave a demo of sparklyr and sparkapi. ** Motivation for the pacakage: The SparkR package overrides the dplyr interface. This is an issue for RStudio. SparkR is not a CRAN package which makes it difficult to add changes. dplyr is the most popular tool by RStudio and is broken on SparkR. ** Sparklyr provides a dplyr interface. It will also support ML like interfaces, such as consuming a ML model. ** Sparklyr does not currently support any distributed computing features. Instead we can recommend ddR as the distributed computing framework on top of sparkapi. We will put the code in CRAN in a couple of weeks. ** Simon: Can you talk more about the wrapper/low level API to work with Spark? *** Javier: The package underneath the cover is called "sparkapi" it is to be used by pacakge builders. "spark_context()" and "invoke()" are the functionality to call scala methods. It does not you to currently run R user defined functions. I am currently working on enabling that feature. Depending upon the interest in using ddR with sparkapi, I can spend more time to make sparkapi feature rich. ** Indrajit: What versions of Spark are supported *** Javier: Anything after 1.6 ** Bernd: How do you export data? *** Javier: We are using all the code from SparkR. So everything in SparkR should continue to work. We don't need to change SparkR. We just need to maintain compatibility. ** Bernd: What happens when the RDDs are very large? *** Javier: Spark will spill on disk. * Michael Kane: Presented examples that he implemented on ddR. ** Talked about how the different distributed packages compare to each other in terms of functionality. ** Michael K. looked at glm and truncated SVD on ddR. Was able to implement irls on ddR by implementing two distributed functions such as "cross". In truncated SVD only needed to overload two different distributed multiplications. ** Ran these algorithms on the 1000 genome dataset. ** Overall liked ddR since it was easy to implement the algorithms in the package. ** New ideas: *** Trying to separate the data layer from the execution layer *** Create an API that works on "chunks" (which is similar to the "parts" API in ddR). Would like to add these APIs to ddR. *** Indrajit: You should be able to get some of the chunk like features by using parts and dmapply. E.g., you can call dmapply to read 10 different files, which correspond 10 chunks now. These are however wrapped as a darray or dframe. But you can continue to work on the individual chunks by using parts(i). === 6/2/2016 === * Round table introduction * (Michael) Goals for the group: ** Make a common abstraction/interfaces to make it easier to work with distributed data and R ** Unify the interface ** Working group will run for a year. Get an API defined, get at least one open source reference implementations ** not everyone needs to work hands on. We will create smaller groups to focus on those aspects. ** We tried to get a diverse group of participants * Logistics: meet monthly, focus groups may meet more often * R Consoritum may be able to figure ways to fund smaller projects that come out of the working group * Michael Kane: Should we start with an inventory of what is available and people are using? ** Michael Lawrence: Yes, we should find the collection of tools as well as the use cases that are common. ** Joe: I will figure out a wiki space. * Javier: Who are the end users? Simon: Common layer needed to get algorithms working. We started from algos and tried to find the minimal common api. One of the goals is to make sure everyone is on the same page and not trying to create his/her own custom interface. * Javier: Should we try to get people with more algo expertise? * Joe: Simon do you have a stack diagram? * Simon: Can we get R Consortium to help write things up and draw things? * Next meeting: Javier is going to present SparkR next time. d8f5f2d4412907360eb12a682d710b9e0fbb9f31 77 74 2017-05-12T02:38:53Z Indrajit roy 14 wikitext text/x-wiki == Goals and Purpose == The Distributed Computing Working Group will endorse the design of a common abstraction for distributed data structures in R. We aim to have at least one open-source implementation, as well as a SQL implementation, released within a year of forming the group. == Members == * '''Michael Lawrence''' (Genentech) * '''Indrajit Roy''' (Google) * ''Joe Rickert'' (ISC liason, RStudio) * Bernd Bischl (LMU) * Matt Dowle (H2O) * Mario Inchiosa (Microsoft) * Michael Kane (Yale) * Javier Luraschi (RStudio) * Edward Ma (HP Enterprise) * Luke Tierney (University of Iowa) * Simon Urbanek (AT&T) * Bryan Lewis (Paradigm4) * Hossein Falaki (databricks) == Reports == [[Distributed Computing Working Group Progress Report 2016|2016 Progress Report]] == Milestones == === Achieved === * Adopt ddR as a prototype for a standard API for distributed computing in R === 2016 Internship === Clark Fitzgerald, a PhD student in the UC Davis Statistics department, worked on ddR and Spark integration. * Wrote [https://github.com/clarkfitzg/sparklite sparklite] and [https://github.com/clarkfitzg/rddlist rddlist] as minimal proof-of-concept R packages to connect and store general data on Spark. [https://docs.google.com/presentation/d/1WfUQ2ockNku90GWMXonEhUEcVOWcgBmWwt5uYSSBYPY/edit?usp=sharing slides] * [https://issues.apache.org/jira/browse/SPARK-16785 Patched SparkR] to allow user defined functions returning binary columns. This allows implementation of different data structures in SparkR. * Updated [https://github.com/vertica/ddR/wiki/Design design documents] with suggested changes to DDR's internal design and object oriented model. * Improved [https://github.com/vertica/ddR/pull/15 testing and ddR internals]. === Outstanding === * Agree on a final standard API for distributed computing in R * Implement at least one scalable backend based on an open-source technology like Spark, SQL, etc == Open Questions == * How can we address the needs of both the end user data scientists and the algorithm implementers? * How should we share data between R and a system like Spark? * Is there any way to unify SparkR and sparklyr? * Could we use the abstractions of tensorflow to partially or fully integrate with platforms like Spark? == Minutes == === 05/11/2017 === * Hernik presented his work on “Futures in R: Atomic Building Blocks for Asynchronous Evaluation” ** Future is an abstraction for a value that will be available later ** Futures are in one of the two states— resolved or unresolved ** Syntax for explicit future: f<- future(expo) v<-value(f) ** There are many ways to resolve futures. E.g., sequential, multicore, multisession, etc. ** Aim is to make the future package “write once, run anywhere”. It works on all platforms. ** In future api, the developer decides what to parallelize and the user decides how to ** future.Batchjobs a future API on top of BatchJobs (map-reduce API for HPC schedulers) ** Hernik presented an example of using DNA-sequence files with the future API. ** Futures can be nested. E.g., one can create a future in an outer loop and create more futures within the inner loop. ** The plan() function can be used with futures to decide whether the futures are run in parallel or sequentially. ** The doFuture() package is a foreach adaptor. It allows foreach to utilize HPC clusters. === 03/09/2017 === * Talk by Brian Lewis ** Has created a Github page with notes on Singularity. ** Singularity is a container technology for HPC applications ** No daemon. Minimum virtualization to get application running. Light weight and has very low overheads. ** Used widely in supercomputers ** All distributed computing platforms even with R skins are difficult to use. ** Containers make it much easier to abstract away the long tail of software dependencies and focus on R ** Demonstrated an example of using Singularity with Tensorflow ** Tried MPI and dopar on the 1000Genome data ** The program parses the variant data and stores chunks as files. Then ran principal components on each file. ** Overload matrix operations to use foreach/MPI underneath. ** Overall: Use existing R operators and overloading them with the appropriate backend. ** Will spend time working on Tensorflow, e.g., take a number of algorithms such as PCA and write them on top of Tensorflow using existing R primitives. === 12/08/2016 === * Yuan Tang from Uptake was the presenter ** Michael and Indrajit will write a status report for the working group sometime in December or January ** Yuan gave an overview of TensorFlow ** JJ, Dirk and Yuan are working on R layer for TensorFlow ** TensorFlow is a platform for machine learning as well as other computations (even math proofs). ** It is GPU optimized and distributed. ** It is used in search, speech recognition, Google photos, etc. ** TensorFlow computations are directed graphs. odes are operations and edges are tensors. ** A lot of array, matrix, etc. operations are available ** Backend is mostly C++. Python front end exists. ** TensorFlow R is based on the python fronted ** In multi-device setting, TensorFlow figures out which devices to use and manages communication between devices. ** Computations are fault tolerant ** Yuan has previously worked on Scikit Flow which is now TF.Learn. It’s a easy transition for Scikit learn users. ** Yuan gave a brief overview of the python interface ** TensorFlow in R handles conversion between R and Python. Syntax is very similar to python API ** Future work: Adding more examples and tutorials, integration with Kubernetes/Marathon like framework. ** During the Q/A there were questions related to whether R kernels can be supported in TensorFlow, and whether R dataframes are a natural wrapper for TensorFlow objects. === 11/10/2016 === * SparkR slides were presented by Hossein Faliki and Shivaram from Databricks and UC Berkeley: ** SparkR was a prototype from AMPLab (2014). Initially it had the RDD API and was similar to PySpark API ** In 2015, the merge with upstream Spark, the decision was made to integrate with the Dataframe API, and hide the RDD API ** In 2016 more MLLib algorithms have been integrated and new APIs have been added. A CRAN package will be released soon ** Original SparkR architecture runs R on the master that communicates with the JVM processes in the driver. the driver sends commands to the worker JVM processes, and executes them as scala/java statements. ** The system can read distributed data inside the JVM from different sources such as S3, HDFS, etc. ** The driver has a socket based connection between SparkR and the RBackend. RBackend runs on the JVM, deserializes the R code, and converts the R statements into Java calls. ** collect() and createDataFrame() are used to move data between R and JVM processes. createDataFrame will convert your local R data into a JVM based distributed data frame. ** The API has IO, Caching, MLLib, and SQL related commands ** Since Spark 2.0, we can run R processes inside the JVM worker processes. There is no need to keep long running R processes. ** There are 3 UDF functions (1) lapply, runs function on different value of a list (2) dapply, runs function on each partition of a data frame. You have to careful about how data is partitioned, and (3) gapply, performs a grouping on different column names and then runs the function on each group. ** The new CRAN package install.spark() will automatically download and install Spark. Automated CRAN checks have been added to every commit to the code. Should be available with Spark 2.1.0 * Q/A ** Currently trying to get zero copy dataframe between python and Spark. Spark 2.0 has an off heap manager that uses Arrow. Once this feature is tested on the Python API, the next step will be integration R. ** Spark dataframes gain from plan optimizations. It is not SparkR specific. R UDFs are still treated as black boxes by the optimizer ** Spark doesn't directly support matrixes. There is no immediate intent to do so either. One can store an array or vector as a single column of a Spark dataframe. === 10/13/2016 === ''Detailed minutes were not taken for this meeting'' * Mario Inchiosa: Microsoft's perspective on distributed computing with R ** Microsoft R Server: abstractions and algorithms for distributed computation on top of open-source R ** Desired features of a distributed API like ddR: *** Supports PEMA (initialize, processData, updateResults, processResults) *** Cross-platform *** Fast runtime *** Supports algorithm writer and data scientist *** Comes with a comprehensive set of algorithms *** Easy deployment ** ddR is making good progress but does not yet meet those requirements * Indrajit: ddR progress report and next steps ** Recap of Clark's internship ** Next step: implement some of Clark's design suggestions: https://github.com/vertica/ddR/wiki/Design ** Spark integration will be based on sparklyr ** Should we limit Spark interaction to the DataFrame API or directly interact with RDDs? *** Consensus: will likely need flexibility of RDDs to implement everything we need, e.g., arrays and lists ** Clark and Javier raised concerns about the scalability of sharing data between R and Spark *** Michael: Spark is a platform in its own right, so interoperability is important, should figure something out *** Bryan Lewis: Why not use tensor abstraction from tensorflow? Spark supports tensorflow and an R interface is already in the works. ** Michael raised the issue of additional funding from the R Consortium to continue Clark's work *** Joe Rickert suggested that the working group develop one or more white papers summarizing the findings of the working group for presentation to the Infrastructure Steering Committee. *** Consensus was in favor of this, and several pointed out that the progress so far has been worthwhile, despite not meeting the specific goals laid out in the proposal. * Michael: do we want to invite some external speakers, one per meeting, from groups like databricks, tensorflow, etc? ** Consensus was in favor. === 9/8/2016 === ''Detailed minutes were not taken for this meeting'' * Clark Fitzgerald: internship report ** Developed two packages for low-level Spark integration: rddlist, sparklite ** Patched a bug in Spark ** ddR needs refactoring before Spark integration is feasible: *** dlist, dframe, and darray should be formal classes. *** Partitions of data should be represented by a distributed list abstraction, and most functions (e.g., dmapply) should be implemented on top of that list. * Javier: sparklyr update ** Preparing for CRAN release ** Mario: what happened to sparkapi? *** Javier: sparkapi has been merged into sparklyr in order to avoid overhead of maintaining two packages. ddR can do everything it needs with sparklyr. * Luke Tierney: Update on the low-level vector abstraction, which might support interfaces like ddR and sparklyr. ** Overall approach seems feasible, but still working out a few details. ** Will land in a branch soon. * Bernd Bischl: update on the batchtools package ** Successor to BatchJobs based on in-memory database === 8/11/2016 === ''Meeting was canceled due to lack of availability.'' === 7/14/2016 === * Introduced Clark who is the intern funded by R Consortium. Clark is a graduate student from UC Davis. He will work on ddR integration with Spark and improving the core ddR API as well such as adding a distributed apply() for matrices, split function, etc. * Bernd: Can I play around with ddR now? What backend should I use? How robust is the code? ** Clark: It's in good enough shape to be played around with. We will continue to improve it. Hopefully the spark integration will be done before the end of my internship in September. * Q: Is anyone working on using ddR to make ML scale better. ** Indrajit: We have kmeans, glm, etc. already in CRAN. ** Michael Kane: We are working on glmnet and other packages related to algorithm development. * Javier gave a demo of sparklyr and sparkapi. ** Motivation for the pacakage: The SparkR package overrides the dplyr interface. This is an issue for RStudio. SparkR is not a CRAN package which makes it difficult to add changes. dplyr is the most popular tool by RStudio and is broken on SparkR. ** Sparklyr provides a dplyr interface. It will also support ML like interfaces, such as consuming a ML model. ** Sparklyr does not currently support any distributed computing features. Instead we can recommend ddR as the distributed computing framework on top of sparkapi. We will put the code in CRAN in a couple of weeks. ** Simon: Can you talk more about the wrapper/low level API to work with Spark? *** Javier: The package underneath the cover is called "sparkapi" it is to be used by pacakge builders. "spark_context()" and "invoke()" are the functionality to call scala methods. It does not you to currently run R user defined functions. I am currently working on enabling that feature. Depending upon the interest in using ddR with sparkapi, I can spend more time to make sparkapi feature rich. ** Indrajit: What versions of Spark are supported *** Javier: Anything after 1.6 ** Bernd: How do you export data? *** Javier: We are using all the code from SparkR. So everything in SparkR should continue to work. We don't need to change SparkR. We just need to maintain compatibility. ** Bernd: What happens when the RDDs are very large? *** Javier: Spark will spill on disk. * Michael Kane: Presented examples that he implemented on ddR. ** Talked about how the different distributed packages compare to each other in terms of functionality. ** Michael K. looked at glm and truncated SVD on ddR. Was able to implement irls on ddR by implementing two distributed functions such as "cross". In truncated SVD only needed to overload two different distributed multiplications. ** Ran these algorithms on the 1000 genome dataset. ** Overall liked ddR since it was easy to implement the algorithms in the package. ** New ideas: *** Trying to separate the data layer from the execution layer *** Create an API that works on "chunks" (which is similar to the "parts" API in ddR). Would like to add these APIs to ddR. *** Indrajit: You should be able to get some of the chunk like features by using parts and dmapply. E.g., you can call dmapply to read 10 different files, which correspond 10 chunks now. These are however wrapped as a darray or dframe. But you can continue to work on the individual chunks by using parts(i). === 6/2/2016 === * Round table introduction * (Michael) Goals for the group: ** Make a common abstraction/interfaces to make it easier to work with distributed data and R ** Unify the interface ** Working group will run for a year. Get an API defined, get at least one open source reference implementations ** not everyone needs to work hands on. We will create smaller groups to focus on those aspects. ** We tried to get a diverse group of participants * Logistics: meet monthly, focus groups may meet more often * R Consoritum may be able to figure ways to fund smaller projects that come out of the working group * Michael Kane: Should we start with an inventory of what is available and people are using? ** Michael Lawrence: Yes, we should find the collection of tools as well as the use cases that are common. ** Joe: I will figure out a wiki space. * Javier: Who are the end users? Simon: Common layer needed to get algorithms working. We started from algos and tried to find the minimal common api. One of the goals is to make sure everyone is on the same page and not trying to create his/her own custom interface. * Javier: Should we try to get people with more algo expertise? * Joe: Simon do you have a stack diagram? * Simon: Can we get R Consortium to help write things up and draw things? * Next meeting: Javier is going to present SparkR next time. 58ef9ccaef0675f275da91892e9e9f8c2e5c5aec 74 72 2017-05-01T18:42:48Z MichaelLawrence 9 wikitext text/x-wiki == Goals and Purpose == The Distributed Computing Working Group will endorse the design of a common abstraction for distributed data structures in R. We aim to have at least one open-source implementation, as well as a SQL implementation, released within a year of forming the group. == Members == * '''Michael Lawrence''' (Genentech) * '''Indrajit Roy''' (HP Enterprise) * ''Joe Rickert'' (ISC liason, RStudio) * Bernd Bischl (LMU) * Matt Dowle (H2O) * Mario Inchiosa (Microsoft) * Michael Kane (Yale) * Javier Luraschi (RStudio) * Edward Ma (HP Enterprise) * Luke Tierney (University of Iowa) * Simon Urbanek (AT&T) * Bryan Lewis (Paradigm4) * Hossein Falaki (databricks) == Reports == [[Distributed Computing Working Group Progress Report 2016|2016 Progress Report]] == Milestones == === Achieved === * Adopt ddR as a prototype for a standard API for distributed computing in R === 2016 Internship === Clark Fitzgerald, a PhD student in the UC Davis Statistics department, worked on ddR and Spark integration. * Wrote [https://github.com/clarkfitzg/sparklite sparklite] and [https://github.com/clarkfitzg/rddlist rddlist] as minimal proof-of-concept R packages to connect and store general data on Spark. [https://docs.google.com/presentation/d/1WfUQ2ockNku90GWMXonEhUEcVOWcgBmWwt5uYSSBYPY/edit?usp=sharing slides] * [https://issues.apache.org/jira/browse/SPARK-16785 Patched SparkR] to allow user defined functions returning binary columns. This allows implementation of different data structures in SparkR. * Updated [https://github.com/vertica/ddR/wiki/Design design documents] with suggested changes to DDR's internal design and object oriented model. * Improved [https://github.com/vertica/ddR/pull/15 testing and ddR internals]. === Outstanding === * Agree on a final standard API for distributed computing in R * Implement at least one scalable backend based on an open-source technology like Spark, SQL, etc == Open Questions == * How can we address the needs of both the end user data scientists and the algorithm implementers? * How should we share data between R and a system like Spark? * Is there any way to unify SparkR and sparklyr? * Could we use the abstractions of tensorflow to partially or fully integrate with platforms like Spark? == Minutes == === 03/09/2017 === * Talk by Brian Lewis ** Has created a Github page with notes on Singularity. ** Singularity is a container technology for HPC applications ** No daemon. Minimum virtualization to get application running. Light weight and has very low overheads. ** Used widely in supercomputers ** All distributed computing platforms even with R skins are difficult to use. ** Containers make it much easier to abstract away the long tail of software dependencies and focus on R ** Demonstrated an example of using Singularity with Tensorflow ** Tried MPI and dopar on the 1000Genome data ** The program parses the variant data and stores chunks as files. Then ran principal components on each file. ** Overload matrix operations to use foreach/MPI underneath. ** Overall: Use existing R operators and overloading them with the appropriate backend. ** Will spend time working on Tensorflow, e.g., take a number of algorithms such as PCA and write them on top of Tensorflow using existing R primitives. === 12/08/2016 === * Yuan Tang from Uptake was the presenter ** Michael and Indrajit will write a status report for the working group sometime in December or January ** Yuan gave an overview of TensorFlow ** JJ, Dirk and Yuan are working on R layer for TensorFlow ** TensorFlow is a platform for machine learning as well as other computations (even math proofs). ** It is GPU optimized and distributed. ** It is used in search, speech recognition, Google photos, etc. ** TensorFlow computations are directed graphs. odes are operations and edges are tensors. ** A lot of array, matrix, etc. operations are available ** Backend is mostly C++. Python front end exists. ** TensorFlow R is based on the python fronted ** In multi-device setting, TensorFlow figures out which devices to use and manages communication between devices. ** Computations are fault tolerant ** Yuan has previously worked on Scikit Flow which is now TF.Learn. It’s a easy transition for Scikit learn users. ** Yuan gave a brief overview of the python interface ** TensorFlow in R handles conversion between R and Python. Syntax is very similar to python API ** Future work: Adding more examples and tutorials, integration with Kubernetes/Marathon like framework. ** During the Q/A there were questions related to whether R kernels can be supported in TensorFlow, and whether R dataframes are a natural wrapper for TensorFlow objects. === 11/10/2016 === * SparkR slides were presented by Hossein Faliki and Shivaram from Databricks and UC Berkeley: ** SparkR was a prototype from AMPLab (2014). Initially it had the RDD API and was similar to PySpark API ** In 2015, the merge with upstream Spark, the decision was made to integrate with the Dataframe API, and hide the RDD API ** In 2016 more MLLib algorithms have been integrated and new APIs have been added. A CRAN package will be released soon ** Original SparkR architecture runs R on the master that communicates with the JVM processes in the driver. the driver sends commands to the worker JVM processes, and executes them as scala/java statements. ** The system can read distributed data inside the JVM from different sources such as S3, HDFS, etc. ** The driver has a socket based connection between SparkR and the RBackend. RBackend runs on the JVM, deserializes the R code, and converts the R statements into Java calls. ** collect() and createDataFrame() are used to move data between R and JVM processes. createDataFrame will convert your local R data into a JVM based distributed data frame. ** The API has IO, Caching, MLLib, and SQL related commands ** Since Spark 2.0, we can run R processes inside the JVM worker processes. There is no need to keep long running R processes. ** There are 3 UDF functions (1) lapply, runs function on different value of a list (2) dapply, runs function on each partition of a data frame. You have to careful about how data is partitioned, and (3) gapply, performs a grouping on different column names and then runs the function on each group. ** The new CRAN package install.spark() will automatically download and install Spark. Automated CRAN checks have been added to every commit to the code. Should be available with Spark 2.1.0 * Q/A ** Currently trying to get zero copy dataframe between python and Spark. Spark 2.0 has an off heap manager that uses Arrow. Once this feature is tested on the Python API, the next step will be integration R. ** Spark dataframes gain from plan optimizations. It is not SparkR specific. R UDFs are still treated as black boxes by the optimizer ** Spark doesn't directly support matrixes. There is no immediate intent to do so either. One can store an array or vector as a single column of a Spark dataframe. === 10/13/2016 === ''Detailed minutes were not taken for this meeting'' * Mario Inchiosa: Microsoft's perspective on distributed computing with R ** Microsoft R Server: abstractions and algorithms for distributed computation on top of open-source R ** Desired features of a distributed API like ddR: *** Supports PEMA (initialize, processData, updateResults, processResults) *** Cross-platform *** Fast runtime *** Supports algorithm writer and data scientist *** Comes with a comprehensive set of algorithms *** Easy deployment ** ddR is making good progress but does not yet meet those requirements * Indrajit: ddR progress report and next steps ** Recap of Clark's internship ** Next step: implement some of Clark's design suggestions: https://github.com/vertica/ddR/wiki/Design ** Spark integration will be based on sparklyr ** Should we limit Spark interaction to the DataFrame API or directly interact with RDDs? *** Consensus: will likely need flexibility of RDDs to implement everything we need, e.g., arrays and lists ** Clark and Javier raised concerns about the scalability of sharing data between R and Spark *** Michael: Spark is a platform in its own right, so interoperability is important, should figure something out *** Bryan Lewis: Why not use tensor abstraction from tensorflow? Spark supports tensorflow and an R interface is already in the works. ** Michael raised the issue of additional funding from the R Consortium to continue Clark's work *** Joe Rickert suggested that the working group develop one or more white papers summarizing the findings of the working group for presentation to the Infrastructure Steering Committee. *** Consensus was in favor of this, and several pointed out that the progress so far has been worthwhile, despite not meeting the specific goals laid out in the proposal. * Michael: do we want to invite some external speakers, one per meeting, from groups like databricks, tensorflow, etc? ** Consensus was in favor. === 9/8/2016 === ''Detailed minutes were not taken for this meeting'' * Clark Fitzgerald: internship report ** Developed two packages for low-level Spark integration: rddlist, sparklite ** Patched a bug in Spark ** ddR needs refactoring before Spark integration is feasible: *** dlist, dframe, and darray should be formal classes. *** Partitions of data should be represented by a distributed list abstraction, and most functions (e.g., dmapply) should be implemented on top of that list. * Javier: sparklyr update ** Preparing for CRAN release ** Mario: what happened to sparkapi? *** Javier: sparkapi has been merged into sparklyr in order to avoid overhead of maintaining two packages. ddR can do everything it needs with sparklyr. * Luke Tierney: Update on the low-level vector abstraction, which might support interfaces like ddR and sparklyr. ** Overall approach seems feasible, but still working out a few details. ** Will land in a branch soon. * Bernd Bischl: update on the batchtools package ** Successor to BatchJobs based on in-memory database === 8/11/2016 === ''Meeting was canceled due to lack of availability.'' === 7/14/2016 === * Introduced Clark who is the intern funded by R Consortium. Clark is a graduate student from UC Davis. He will work on ddR integration with Spark and improving the core ddR API as well such as adding a distributed apply() for matrices, split function, etc. * Bernd: Can I play around with ddR now? What backend should I use? How robust is the code? ** Clark: It's in good enough shape to be played around with. We will continue to improve it. Hopefully the spark integration will be done before the end of my internship in September. * Q: Is anyone working on using ddR to make ML scale better. ** Indrajit: We have kmeans, glm, etc. already in CRAN. ** Michael Kane: We are working on glmnet and other packages related to algorithm development. * Javier gave a demo of sparklyr and sparkapi. ** Motivation for the pacakage: The SparkR package overrides the dplyr interface. This is an issue for RStudio. SparkR is not a CRAN package which makes it difficult to add changes. dplyr is the most popular tool by RStudio and is broken on SparkR. ** Sparklyr provides a dplyr interface. It will also support ML like interfaces, such as consuming a ML model. ** Sparklyr does not currently support any distributed computing features. Instead we can recommend ddR as the distributed computing framework on top of sparkapi. We will put the code in CRAN in a couple of weeks. ** Simon: Can you talk more about the wrapper/low level API to work with Spark? *** Javier: The package underneath the cover is called "sparkapi" it is to be used by pacakge builders. "spark_context()" and "invoke()" are the functionality to call scala methods. It does not you to currently run R user defined functions. I am currently working on enabling that feature. Depending upon the interest in using ddR with sparkapi, I can spend more time to make sparkapi feature rich. ** Indrajit: What versions of Spark are supported *** Javier: Anything after 1.6 ** Bernd: How do you export data? *** Javier: We are using all the code from SparkR. So everything in SparkR should continue to work. We don't need to change SparkR. We just need to maintain compatibility. ** Bernd: What happens when the RDDs are very large? *** Javier: Spark will spill on disk. * Michael Kane: Presented examples that he implemented on ddR. ** Talked about how the different distributed packages compare to each other in terms of functionality. ** Michael K. looked at glm and truncated SVD on ddR. Was able to implement irls on ddR by implementing two distributed functions such as "cross". In truncated SVD only needed to overload two different distributed multiplications. ** Ran these algorithms on the 1000 genome dataset. ** Overall liked ddR since it was easy to implement the algorithms in the package. ** New ideas: *** Trying to separate the data layer from the execution layer *** Create an API that works on "chunks" (which is similar to the "parts" API in ddR). Would like to add these APIs to ddR. *** Indrajit: You should be able to get some of the chunk like features by using parts and dmapply. E.g., you can call dmapply to read 10 different files, which correspond 10 chunks now. These are however wrapped as a darray or dframe. But you can continue to work on the individual chunks by using parts(i). === 6/2/2016 === * Round table introduction * (Michael) Goals for the group: ** Make a common abstraction/interfaces to make it easier to work with distributed data and R ** Unify the interface ** Working group will run for a year. Get an API defined, get at least one open source reference implementations ** not everyone needs to work hands on. We will create smaller groups to focus on those aspects. ** We tried to get a diverse group of participants * Logistics: meet monthly, focus groups may meet more often * R Consoritum may be able to figure ways to fund smaller projects that come out of the working group * Michael Kane: Should we start with an inventory of what is available and people are using? ** Michael Lawrence: Yes, we should find the collection of tools as well as the use cases that are common. ** Joe: I will figure out a wiki space. * Javier: Who are the end users? Simon: Common layer needed to get algorithms working. We started from algos and tried to find the minimal common api. One of the goals is to make sure everyone is on the same page and not trying to create his/her own custom interface. * Javier: Should we try to get people with more algo expertise? * Joe: Simon do you have a stack diagram? * Simon: Can we get R Consortium to help write things up and draw things? * Next meeting: Javier is going to present SparkR next time. a32e7c959b0b686142aee46a5d9f176cacaffff5 72 70 2017-05-01T17:56:12Z MichaelLawrence 9 wikitext text/x-wiki == Goals and Purpose == The Distributed Computing Working Group will endorse the design of a common abstraction for distributed data structures in R. We aim to have at least one open-source implementation, as well as a SQL implementation, released within a year of forming the group. == Members == * '''Michael Lawrence''' (Genentech) * '''Indrajit Roy''' (HP Enterprise) * ''Joe Rickert'' (ISC liason, RStudio) * Bernd Bischl (LMU) * Matt Dowle (H2O) * Mario Inchiosa (Microsoft) * Michael Kane (Yale) * Javier Luraschi (RStudio) * Edward Ma (HP Enterprise) * Luke Tierney (University of Iowa) * Simon Urbanek (AT&T) * Bryan Lewis (Paradigm4) * Hossein Falaki (databricks) == Reports == [[ddr2016|2016 Progress Report]] == Milestones == === Achieved === * Adopt ddR as a prototype for a standard API for distributed computing in R === 2016 Internship === Clark Fitzgerald, a PhD student in the UC Davis Statistics department, worked on ddR and Spark integration. * Wrote [https://github.com/clarkfitzg/sparklite sparklite] and [https://github.com/clarkfitzg/rddlist rddlist] as minimal proof-of-concept R packages to connect and store general data on Spark. [https://docs.google.com/presentation/d/1WfUQ2ockNku90GWMXonEhUEcVOWcgBmWwt5uYSSBYPY/edit?usp=sharing slides] * [https://issues.apache.org/jira/browse/SPARK-16785 Patched SparkR] to allow user defined functions returning binary columns. This allows implementation of different data structures in SparkR. * Updated [https://github.com/vertica/ddR/wiki/Design design documents] with suggested changes to DDR's internal design and object oriented model. * Improved [https://github.com/vertica/ddR/pull/15 testing and ddR internals]. === Outstanding === * Agree on a final standard API for distributed computing in R * Implement at least one scalable backend based on an open-source technology like Spark, SQL, etc == Open Questions == * How can we address the needs of both the end user data scientists and the algorithm implementers? * How should we share data between R and a system like Spark? * Is there any way to unify SparkR and sparklyr? * Could we use the abstractions of tensorflow to partially or fully integrate with platforms like Spark? == Minutes == === 03/09/2017 === * Talk by Brian Lewis ** Has created a Github page with notes on Singularity. ** Singularity is a container technology for HPC applications ** No daemon. Minimum virtualization to get application running. Light weight and has very low overheads. ** Used widely in supercomputers ** All distributed computing platforms even with R skins are difficult to use. ** Containers make it much easier to abstract away the long tail of software dependencies and focus on R ** Demonstrated an example of using Singularity with Tensorflow ** Tried MPI and dopar on the 1000Genome data ** The program parses the variant data and stores chunks as files. Then ran principal components on each file. ** Overload matrix operations to use foreach/MPI underneath. ** Overall: Use existing R operators and overloading them with the appropriate backend. ** Will spend time working on Tensorflow, e.g., take a number of algorithms such as PCA and write them on top of Tensorflow using existing R primitives. === 12/08/2016 === * Yuan Tang from Uptake was the presenter ** Michael and Indrajit will write a status report for the working group sometime in December or January ** Yuan gave an overview of TensorFlow ** JJ, Dirk and Yuan are working on R layer for TensorFlow ** TensorFlow is a platform for machine learning as well as other computations (even math proofs). ** It is GPU optimized and distributed. ** It is used in search, speech recognition, Google photos, etc. ** TensorFlow computations are directed graphs. odes are operations and edges are tensors. ** A lot of array, matrix, etc. operations are available ** Backend is mostly C++. Python front end exists. ** TensorFlow R is based on the python fronted ** In multi-device setting, TensorFlow figures out which devices to use and manages communication between devices. ** Computations are fault tolerant ** Yuan has previously worked on Scikit Flow which is now TF.Learn. It’s a easy transition for Scikit learn users. ** Yuan gave a brief overview of the python interface ** TensorFlow in R handles conversion between R and Python. Syntax is very similar to python API ** Future work: Adding more examples and tutorials, integration with Kubernetes/Marathon like framework. ** During the Q/A there were questions related to whether R kernels can be supported in TensorFlow, and whether R dataframes are a natural wrapper for TensorFlow objects. === 11/10/2016 === * SparkR slides were presented by Hossein Faliki and Shivaram from Databricks and UC Berkeley: ** SparkR was a prototype from AMPLab (2014). Initially it had the RDD API and was similar to PySpark API ** In 2015, the merge with upstream Spark, the decision was made to integrate with the Dataframe API, and hide the RDD API ** In 2016 more MLLib algorithms have been integrated and new APIs have been added. A CRAN package will be released soon ** Original SparkR architecture runs R on the master that communicates with the JVM processes in the driver. the driver sends commands to the worker JVM processes, and executes them as scala/java statements. ** The system can read distributed data inside the JVM from different sources such as S3, HDFS, etc. ** The driver has a socket based connection between SparkR and the RBackend. RBackend runs on the JVM, deserializes the R code, and converts the R statements into Java calls. ** collect() and createDataFrame() are used to move data between R and JVM processes. createDataFrame will convert your local R data into a JVM based distributed data frame. ** The API has IO, Caching, MLLib, and SQL related commands ** Since Spark 2.0, we can run R processes inside the JVM worker processes. There is no need to keep long running R processes. ** There are 3 UDF functions (1) lapply, runs function on different value of a list (2) dapply, runs function on each partition of a data frame. You have to careful about how data is partitioned, and (3) gapply, performs a grouping on different column names and then runs the function on each group. ** The new CRAN package install.spark() will automatically download and install Spark. Automated CRAN checks have been added to every commit to the code. Should be available with Spark 2.1.0 * Q/A ** Currently trying to get zero copy dataframe between python and Spark. Spark 2.0 has an off heap manager that uses Arrow. Once this feature is tested on the Python API, the next step will be integration R. ** Spark dataframes gain from plan optimizations. It is not SparkR specific. R UDFs are still treated as black boxes by the optimizer ** Spark doesn't directly support matrixes. There is no immediate intent to do so either. One can store an array or vector as a single column of a Spark dataframe. === 10/13/2016 === ''Detailed minutes were not taken for this meeting'' * Mario Inchiosa: Microsoft's perspective on distributed computing with R ** Microsoft R Server: abstractions and algorithms for distributed computation on top of open-source R ** Desired features of a distributed API like ddR: *** Supports PEMA (initialize, processData, updateResults, processResults) *** Cross-platform *** Fast runtime *** Supports algorithm writer and data scientist *** Comes with a comprehensive set of algorithms *** Easy deployment ** ddR is making good progress but does not yet meet those requirements * Indrajit: ddR progress report and next steps ** Recap of Clark's internship ** Next step: implement some of Clark's design suggestions: https://github.com/vertica/ddR/wiki/Design ** Spark integration will be based on sparklyr ** Should we limit Spark interaction to the DataFrame API or directly interact with RDDs? *** Consensus: will likely need flexibility of RDDs to implement everything we need, e.g., arrays and lists ** Clark and Javier raised concerns about the scalability of sharing data between R and Spark *** Michael: Spark is a platform in its own right, so interoperability is important, should figure something out *** Bryan Lewis: Why not use tensor abstraction from tensorflow? Spark supports tensorflow and an R interface is already in the works. ** Michael raised the issue of additional funding from the R Consortium to continue Clark's work *** Joe Rickert suggested that the working group develop one or more white papers summarizing the findings of the working group for presentation to the Infrastructure Steering Committee. *** Consensus was in favor of this, and several pointed out that the progress so far has been worthwhile, despite not meeting the specific goals laid out in the proposal. * Michael: do we want to invite some external speakers, one per meeting, from groups like databricks, tensorflow, etc? ** Consensus was in favor. === 9/8/2016 === ''Detailed minutes were not taken for this meeting'' * Clark Fitzgerald: internship report ** Developed two packages for low-level Spark integration: rddlist, sparklite ** Patched a bug in Spark ** ddR needs refactoring before Spark integration is feasible: *** dlist, dframe, and darray should be formal classes. *** Partitions of data should be represented by a distributed list abstraction, and most functions (e.g., dmapply) should be implemented on top of that list. * Javier: sparklyr update ** Preparing for CRAN release ** Mario: what happened to sparkapi? *** Javier: sparkapi has been merged into sparklyr in order to avoid overhead of maintaining two packages. ddR can do everything it needs with sparklyr. * Luke Tierney: Update on the low-level vector abstraction, which might support interfaces like ddR and sparklyr. ** Overall approach seems feasible, but still working out a few details. ** Will land in a branch soon. * Bernd Bischl: update on the batchtools package ** Successor to BatchJobs based on in-memory database === 8/11/2016 === ''Meeting was canceled due to lack of availability.'' === 7/14/2016 === * Introduced Clark who is the intern funded by R Consortium. Clark is a graduate student from UC Davis. He will work on ddR integration with Spark and improving the core ddR API as well such as adding a distributed apply() for matrices, split function, etc. * Bernd: Can I play around with ddR now? What backend should I use? How robust is the code? ** Clark: It's in good enough shape to be played around with. We will continue to improve it. Hopefully the spark integration will be done before the end of my internship in September. * Q: Is anyone working on using ddR to make ML scale better. ** Indrajit: We have kmeans, glm, etc. already in CRAN. ** Michael Kane: We are working on glmnet and other packages related to algorithm development. * Javier gave a demo of sparklyr and sparkapi. ** Motivation for the pacakage: The SparkR package overrides the dplyr interface. This is an issue for RStudio. SparkR is not a CRAN package which makes it difficult to add changes. dplyr is the most popular tool by RStudio and is broken on SparkR. ** Sparklyr provides a dplyr interface. It will also support ML like interfaces, such as consuming a ML model. ** Sparklyr does not currently support any distributed computing features. Instead we can recommend ddR as the distributed computing framework on top of sparkapi. We will put the code in CRAN in a couple of weeks. ** Simon: Can you talk more about the wrapper/low level API to work with Spark? *** Javier: The package underneath the cover is called "sparkapi" it is to be used by pacakge builders. "spark_context()" and "invoke()" are the functionality to call scala methods. It does not you to currently run R user defined functions. I am currently working on enabling that feature. Depending upon the interest in using ddR with sparkapi, I can spend more time to make sparkapi feature rich. ** Indrajit: What versions of Spark are supported *** Javier: Anything after 1.6 ** Bernd: How do you export data? *** Javier: We are using all the code from SparkR. So everything in SparkR should continue to work. We don't need to change SparkR. We just need to maintain compatibility. ** Bernd: What happens when the RDDs are very large? *** Javier: Spark will spill on disk. * Michael Kane: Presented examples that he implemented on ddR. ** Talked about how the different distributed packages compare to each other in terms of functionality. ** Michael K. looked at glm and truncated SVD on ddR. Was able to implement irls on ddR by implementing two distributed functions such as "cross". In truncated SVD only needed to overload two different distributed multiplications. ** Ran these algorithms on the 1000 genome dataset. ** Overall liked ddR since it was easy to implement the algorithms in the package. ** New ideas: *** Trying to separate the data layer from the execution layer *** Create an API that works on "chunks" (which is similar to the "parts" API in ddR). Would like to add these APIs to ddR. *** Indrajit: You should be able to get some of the chunk like features by using parts and dmapply. E.g., you can call dmapply to read 10 different files, which correspond 10 chunks now. These are however wrapped as a darray or dframe. But you can continue to work on the individual chunks by using parts(i). === 6/2/2016 === * Round table introduction * (Michael) Goals for the group: ** Make a common abstraction/interfaces to make it easier to work with distributed data and R ** Unify the interface ** Working group will run for a year. Get an API defined, get at least one open source reference implementations ** not everyone needs to work hands on. We will create smaller groups to focus on those aspects. ** We tried to get a diverse group of participants * Logistics: meet monthly, focus groups may meet more often * R Consoritum may be able to figure ways to fund smaller projects that come out of the working group * Michael Kane: Should we start with an inventory of what is available and people are using? ** Michael Lawrence: Yes, we should find the collection of tools as well as the use cases that are common. ** Joe: I will figure out a wiki space. * Javier: Who are the end users? Simon: Common layer needed to get algorithms working. We started from algos and tried to find the minimal common api. One of the goals is to make sure everyone is on the same page and not trying to create his/her own custom interface. * Javier: Should we try to get people with more algo expertise? * Joe: Simon do you have a stack diagram? * Simon: Can we get R Consortium to help write things up and draw things? * Next meeting: Javier is going to present SparkR next time. 4ea35212c422459c7fc2721f601e7237076a2b54 70 57 2017-03-09T18:47:58Z Indrajit roy 14 wikitext text/x-wiki == Goals and Purpose == The Distributed Computing Working Group will endorse the design of a common abstraction for distributed data structures in R. We aim to have at least one open-source implementation, as well as a SQL implementation, released within a year of forming the group. == Members == * '''Michael Lawrence''' (Genentech) * '''Indrajit Roy''' (HP Enterprise) * ''Joe Rickert'' (ISC liason, RStudio) * Bernd Bischl (LMU) * Matt Dowle (H2O) * Mario Inchiosa (Microsoft) * Michael Kane (Yale) * Javier Luraschi (RStudio) * Edward Ma (HP Enterprise) * Luke Tierney (University of Iowa) * Simon Urbanek (AT&T) * Bryan Lewis (Paradigm4) * Hossein Falaki (databricks) == Milestones == === Achieved === * Adopt ddR as a prototype for a standard API for distributed computing in R === 2016 Internship === Clark Fitzgerald, a PhD student in the UC Davis Statistics department, worked on ddR and Spark integration. * Wrote [https://github.com/clarkfitzg/sparklite sparklite] and [https://github.com/clarkfitzg/rddlist rddlist] as minimal proof-of-concept R packages to connect and store general data on Spark. [https://docs.google.com/presentation/d/1WfUQ2ockNku90GWMXonEhUEcVOWcgBmWwt5uYSSBYPY/edit?usp=sharing slides] * [https://issues.apache.org/jira/browse/SPARK-16785 Patched SparkR] to allow user defined functions returning binary columns. This allows implementation of different data structures in SparkR. * Updated [https://github.com/vertica/ddR/wiki/Design design documents] with suggested changes to DDR's internal design and object oriented model. * Improved [https://github.com/vertica/ddR/pull/15 testing and ddR internals]. === Outstanding === * Agree on a final standard API for distributed computing in R * Implement at least one scalable backend based on an open-source technology like Spark, SQL, etc == Open Questions == * How can we address the needs of both the end user data scientists and the algorithm implementers? * How should we share data between R and a system like Spark? * Is there any way to unify SparkR and sparklyr? * Could we use the abstractions of tensorflow to partially or fully integrate with platforms like Spark? == Minutes == === 03/09/2017 === * Talk by Brian Lewis ** Has created a Github page with notes on Singularity. ** Singularity is a container technology for HPC applications ** No daemon. Minimum virtualization to get application running. Light weight and has very low overheads. ** Used widely in supercomputers ** All distributed computing platforms even with R skins are difficult to use. ** Containers make it much easier to abstract away the long tail of software dependencies and focus on R ** Demonstrated an example of using Singularity with Tensorflow ** Tried MPI and dopar on the 1000Genome data ** The program parses the variant data and stores chunks as files. Then ran principal components on each file. ** Overload matrix operations to use foreach/MPI underneath. ** Overall: Use existing R operators and overloading them with the appropriate backend. ** Will spend time working on Tensorflow, e.g., take a number of algorithms such as PCA and write them on top of Tensorflow using existing R primitives. === 12/08/2016 === * Yuan Tang from Uptake was the presenter ** Michael and Indrajit will write a status report for the working group sometime in December or January ** Yuan gave an overview of TensorFlow ** JJ, Dirk and Yuan are working on R layer for TensorFlow ** TensorFlow is a platform for machine learning as well as other computations (even math proofs). ** It is GPU optimized and distributed. ** It is used in search, speech recognition, Google photos, etc. ** TensorFlow computations are directed graphs. odes are operations and edges are tensors. ** A lot of array, matrix, etc. operations are available ** Backend is mostly C++. Python front end exists. ** TensorFlow R is based on the python fronted ** In multi-device setting, TensorFlow figures out which devices to use and manages communication between devices. ** Computations are fault tolerant ** Yuan has previously worked on Scikit Flow which is now TF.Learn. It’s a easy transition for Scikit learn users. ** Yuan gave a brief overview of the python interface ** TensorFlow in R handles conversion between R and Python. Syntax is very similar to python API ** Future work: Adding more examples and tutorials, integration with Kubernetes/Marathon like framework. ** During the Q/A there were questions related to whether R kernels can be supported in TensorFlow, and whether R dataframes are a natural wrapper for TensorFlow objects. === 11/10/2016 === * SparkR slides were presented by Hossein Faliki and Shivaram from Databricks and UC Berkeley: ** SparkR was a prototype from AMPLab (2014). Initially it had the RDD API and was similar to PySpark API ** In 2015, the merge with upstream Spark, the decision was made to integrate with the Dataframe API, and hide the RDD API ** In 2016 more MLLib algorithms have been integrated and new APIs have been added. A CRAN package will be released soon ** Original SparkR architecture runs R on the master that communicates with the JVM processes in the driver. the driver sends commands to the worker JVM processes, and executes them as scala/java statements. ** The system can read distributed data inside the JVM from different sources such as S3, HDFS, etc. ** The driver has a socket based connection between SparkR and the RBackend. RBackend runs on the JVM, deserializes the R code, and converts the R statements into Java calls. ** collect() and createDataFrame() are used to move data between R and JVM processes. createDataFrame will convert your local R data into a JVM based distributed data frame. ** The API has IO, Caching, MLLib, and SQL related commands ** Since Spark 2.0, we can run R processes inside the JVM worker processes. There is no need to keep long running R processes. ** There are 3 UDF functions (1) lapply, runs function on different value of a list (2) dapply, runs function on each partition of a data frame. You have to careful about how data is partitioned, and (3) gapply, performs a grouping on different column names and then runs the function on each group. ** The new CRAN package install.spark() will automatically download and install Spark. Automated CRAN checks have been added to every commit to the code. Should be available with Spark 2.1.0 * Q/A ** Currently trying to get zero copy dataframe between python and Spark. Spark 2.0 has an off heap manager that uses Arrow. Once this feature is tested on the Python API, the next step will be integration R. ** Spark dataframes gain from plan optimizations. It is not SparkR specific. R UDFs are still treated as black boxes by the optimizer ** Spark doesn't directly support matrixes. There is no immediate intent to do so either. One can store an array or vector as a single column of a Spark dataframe. === 10/13/2016 === ''Detailed minutes were not taken for this meeting'' * Mario Inchiosa: Microsoft's perspective on distributed computing with R ** Microsoft R Server: abstractions and algorithms for distributed computation on top of open-source R ** Desired features of a distributed API like ddR: *** Supports PEMA (initialize, processData, updateResults, processResults) *** Cross-platform *** Fast runtime *** Supports algorithm writer and data scientist *** Comes with a comprehensive set of algorithms *** Easy deployment ** ddR is making good progress but does not yet meet those requirements * Indrajit: ddR progress report and next steps ** Recap of Clark's internship ** Next step: implement some of Clark's design suggestions: https://github.com/vertica/ddR/wiki/Design ** Spark integration will be based on sparklyr ** Should we limit Spark interaction to the DataFrame API or directly interact with RDDs? *** Consensus: will likely need flexibility of RDDs to implement everything we need, e.g., arrays and lists ** Clark and Javier raised concerns about the scalability of sharing data between R and Spark *** Michael: Spark is a platform in its own right, so interoperability is important, should figure something out *** Bryan Lewis: Why not use tensor abstraction from tensorflow? Spark supports tensorflow and an R interface is already in the works. ** Michael raised the issue of additional funding from the R Consortium to continue Clark's work *** Joe Rickert suggested that the working group develop one or more white papers summarizing the findings of the working group for presentation to the Infrastructure Steering Committee. *** Consensus was in favor of this, and several pointed out that the progress so far has been worthwhile, despite not meeting the specific goals laid out in the proposal. * Michael: do we want to invite some external speakers, one per meeting, from groups like databricks, tensorflow, etc? ** Consensus was in favor. === 9/8/2016 === ''Detailed minutes were not taken for this meeting'' * Clark Fitzgerald: internship report ** Developed two packages for low-level Spark integration: rddlist, sparklite ** Patched a bug in Spark ** ddR needs refactoring before Spark integration is feasible: *** dlist, dframe, and darray should be formal classes. *** Partitions of data should be represented by a distributed list abstraction, and most functions (e.g., dmapply) should be implemented on top of that list. * Javier: sparklyr update ** Preparing for CRAN release ** Mario: what happened to sparkapi? *** Javier: sparkapi has been merged into sparklyr in order to avoid overhead of maintaining two packages. ddR can do everything it needs with sparklyr. * Luke Tierney: Update on the low-level vector abstraction, which might support interfaces like ddR and sparklyr. ** Overall approach seems feasible, but still working out a few details. ** Will land in a branch soon. * Bernd Bischl: update on the batchtools package ** Successor to BatchJobs based on in-memory database === 8/11/2016 === ''Meeting was canceled due to lack of availability.'' === 7/14/2016 === * Introduced Clark who is the intern funded by R Consortium. Clark is a graduate student from UC Davis. He will work on ddR integration with Spark and improving the core ddR API as well such as adding a distributed apply() for matrices, split function, etc. * Bernd: Can I play around with ddR now? What backend should I use? How robust is the code? ** Clark: It's in good enough shape to be played around with. We will continue to improve it. Hopefully the spark integration will be done before the end of my internship in September. * Q: Is anyone working on using ddR to make ML scale better. ** Indrajit: We have kmeans, glm, etc. already in CRAN. ** Michael Kane: We are working on glmnet and other packages related to algorithm development. * Javier gave a demo of sparklyr and sparkapi. ** Motivation for the pacakage: The SparkR package overrides the dplyr interface. This is an issue for RStudio. SparkR is not a CRAN package which makes it difficult to add changes. dplyr is the most popular tool by RStudio and is broken on SparkR. ** Sparklyr provides a dplyr interface. It will also support ML like interfaces, such as consuming a ML model. ** Sparklyr does not currently support any distributed computing features. Instead we can recommend ddR as the distributed computing framework on top of sparkapi. We will put the code in CRAN in a couple of weeks. ** Simon: Can you talk more about the wrapper/low level API to work with Spark? *** Javier: The package underneath the cover is called "sparkapi" it is to be used by pacakge builders. "spark_context()" and "invoke()" are the functionality to call scala methods. It does not you to currently run R user defined functions. I am currently working on enabling that feature. Depending upon the interest in using ddR with sparkapi, I can spend more time to make sparkapi feature rich. ** Indrajit: What versions of Spark are supported *** Javier: Anything after 1.6 ** Bernd: How do you export data? *** Javier: We are using all the code from SparkR. So everything in SparkR should continue to work. We don't need to change SparkR. We just need to maintain compatibility. ** Bernd: What happens when the RDDs are very large? *** Javier: Spark will spill on disk. * Michael Kane: Presented examples that he implemented on ddR. ** Talked about how the different distributed packages compare to each other in terms of functionality. ** Michael K. looked at glm and truncated SVD on ddR. Was able to implement irls on ddR by implementing two distributed functions such as "cross". In truncated SVD only needed to overload two different distributed multiplications. ** Ran these algorithms on the 1000 genome dataset. ** Overall liked ddR since it was easy to implement the algorithms in the package. ** New ideas: *** Trying to separate the data layer from the execution layer *** Create an API that works on "chunks" (which is similar to the "parts" API in ddR). Would like to add these APIs to ddR. *** Indrajit: You should be able to get some of the chunk like features by using parts and dmapply. E.g., you can call dmapply to read 10 different files, which correspond 10 chunks now. These are however wrapped as a darray or dframe. But you can continue to work on the individual chunks by using parts(i). === 6/2/2016 === * Round table introduction * (Michael) Goals for the group: ** Make a common abstraction/interfaces to make it easier to work with distributed data and R ** Unify the interface ** Working group will run for a year. Get an API defined, get at least one open source reference implementations ** not everyone needs to work hands on. We will create smaller groups to focus on those aspects. ** We tried to get a diverse group of participants * Logistics: meet monthly, focus groups may meet more often * R Consoritum may be able to figure ways to fund smaller projects that come out of the working group * Michael Kane: Should we start with an inventory of what is available and people are using? ** Michael Lawrence: Yes, we should find the collection of tools as well as the use cases that are common. ** Joe: I will figure out a wiki space. * Javier: Who are the end users? Simon: Common layer needed to get algorithms working. We started from algos and tried to find the minimal common api. One of the goals is to make sure everyone is on the same page and not trying to create his/her own custom interface. * Javier: Should we try to get people with more algo expertise? * Joe: Simon do you have a stack diagram? * Simon: Can we get R Consortium to help write things up and draw things? * Next meeting: Javier is going to present SparkR next time. f5a3b7e0a40fda707980c4f44821f041495c4e2a 57 56 2016-12-09T18:48:40Z MichaelLawrence 9 Yuan works at Uptake wikitext text/x-wiki == Goals and Purpose == The Distributed Computing Working Group will endorse the design of a common abstraction for distributed data structures in R. We aim to have at least one open-source implementation, as well as a SQL implementation, released within a year of forming the group. == Members == * '''Michael Lawrence''' (Genentech) * '''Indrajit Roy''' (HP Enterprise) * ''Joe Rickert'' (ISC liason, RStudio) * Bernd Bischl (LMU) * Matt Dowle (H2O) * Mario Inchiosa (Microsoft) * Michael Kane (Yale) * Javier Luraschi (RStudio) * Edward Ma (HP Enterprise) * Luke Tierney (University of Iowa) * Simon Urbanek (AT&T) * Bryan Lewis (Paradigm4) * Hossein Falaki (databricks) == Milestones == === Achieved === * Adopt ddR as a prototype for a standard API for distributed computing in R === 2016 Internship === Clark Fitzgerald, a PhD student in the UC Davis Statistics department, worked on ddR and Spark integration. * Wrote [https://github.com/clarkfitzg/sparklite sparklite] and [https://github.com/clarkfitzg/rddlist rddlist] as minimal proof-of-concept R packages to connect and store general data on Spark. [https://docs.google.com/presentation/d/1WfUQ2ockNku90GWMXonEhUEcVOWcgBmWwt5uYSSBYPY/edit?usp=sharing slides] * [https://issues.apache.org/jira/browse/SPARK-16785 Patched SparkR] to allow user defined functions returning binary columns. This allows implementation of different data structures in SparkR. * Updated [https://github.com/vertica/ddR/wiki/Design design documents] with suggested changes to DDR's internal design and object oriented model. * Improved [https://github.com/vertica/ddR/pull/15 testing and ddR internals]. === Outstanding === * Agree on a final standard API for distributed computing in R * Implement at least one scalable backend based on an open-source technology like Spark, SQL, etc == Open Questions == * How can we address the needs of both the end user data scientists and the algorithm implementers? * How should we share data between R and a system like Spark? * Is there any way to unify SparkR and sparklyr? * Could we use the abstractions of tensorflow to partially or fully integrate with platforms like Spark? == Minutes == === 12/08/2016 === * Yuan Tang from Uptake was the presenter ** Michael and Indrajit will write a status report for the working group sometime in December or January ** Yuan gave an overview of TensorFlow ** JJ, Dirk and Yuan are working on R layer for TensorFlow ** TensorFlow is a platform for machine learning as well as other computations (even math proofs). ** It is GPU optimized and distributed. ** It is used in search, speech recognition, Google photos, etc. ** TensorFlow computations are directed graphs. odes are operations and edges are tensors. ** A lot of array, matrix, etc. operations are available ** Backend is mostly C++. Python front end exists. ** TensorFlow R is based on the python fronted ** In multi-device setting, TensorFlow figures out which devices to use and manages communication between devices. ** Computations are fault tolerant ** Yuan has previously worked on Scikit Flow which is now TF.Learn. It’s a easy transition for Scikit learn users. ** Yuan gave a brief overview of the python interface ** TensorFlow in R handles conversion between R and Python. Syntax is very similar to python API ** Future work: Adding more examples and tutorials, integration with Kubernetes/Marathon like framework. ** During the Q/A there were questions related to whether R kernels can be supported in TensorFlow, and whether R dataframes are a natural wrapper for TensorFlow objects. === 11/10/2016 === * SparkR slides were presented by Hossein Faliki and Shivaram from Databricks and UC Berkeley: ** SparkR was a prototype from AMPLab (2014). Initially it had the RDD API and was similar to PySpark API ** In 2015, the merge with upstream Spark, the decision was made to integrate with the Dataframe API, and hide the RDD API ** In 2016 more MLLib algorithms have been integrated and new APIs have been added. A CRAN package will be released soon ** Original SparkR architecture runs R on the master that communicates with the JVM processes in the driver. the driver sends commands to the worker JVM processes, and executes them as scala/java statements. ** The system can read distributed data inside the JVM from different sources such as S3, HDFS, etc. ** The driver has a socket based connection between SparkR and the RBackend. RBackend runs on the JVM, deserializes the R code, and converts the R statements into Java calls. ** collect() and createDataFrame() are used to move data between R and JVM processes. createDataFrame will convert your local R data into a JVM based distributed data frame. ** The API has IO, Caching, MLLib, and SQL related commands ** Since Spark 2.0, we can run R processes inside the JVM worker processes. There is no need to keep long running R processes. ** There are 3 UDF functions (1) lapply, runs function on different value of a list (2) dapply, runs function on each partition of a data frame. You have to careful about how data is partitioned, and (3) gapply, performs a grouping on different column names and then runs the function on each group. ** The new CRAN package install.spark() will automatically download and install Spark. Automated CRAN checks have been added to every commit to the code. Should be available with Spark 2.1.0 * Q/A ** Currently trying to get zero copy dataframe between python and Spark. Spark 2.0 has an off heap manager that uses Arrow. Once this feature is tested on the Python API, the next step will be integration R. ** Spark dataframes gain from plan optimizations. It is not SparkR specific. R UDFs are still treated as black boxes by the optimizer ** Spark doesn't directly support matrixes. There is no immediate intent to do so either. One can store an array or vector as a single column of a Spark dataframe. === 10/13/2016 === ''Detailed minutes were not taken for this meeting'' * Mario Inchiosa: Microsoft's perspective on distributed computing with R ** Microsoft R Server: abstractions and algorithms for distributed computation on top of open-source R ** Desired features of a distributed API like ddR: *** Supports PEMA (initialize, processData, updateResults, processResults) *** Cross-platform *** Fast runtime *** Supports algorithm writer and data scientist *** Comes with a comprehensive set of algorithms *** Easy deployment ** ddR is making good progress but does not yet meet those requirements * Indrajit: ddR progress report and next steps ** Recap of Clark's internship ** Next step: implement some of Clark's design suggestions: https://github.com/vertica/ddR/wiki/Design ** Spark integration will be based on sparklyr ** Should we limit Spark interaction to the DataFrame API or directly interact with RDDs? *** Consensus: will likely need flexibility of RDDs to implement everything we need, e.g., arrays and lists ** Clark and Javier raised concerns about the scalability of sharing data between R and Spark *** Michael: Spark is a platform in its own right, so interoperability is important, should figure something out *** Bryan Lewis: Why not use tensor abstraction from tensorflow? Spark supports tensorflow and an R interface is already in the works. ** Michael raised the issue of additional funding from the R Consortium to continue Clark's work *** Joe Rickert suggested that the working group develop one or more white papers summarizing the findings of the working group for presentation to the Infrastructure Steering Committee. *** Consensus was in favor of this, and several pointed out that the progress so far has been worthwhile, despite not meeting the specific goals laid out in the proposal. * Michael: do we want to invite some external speakers, one per meeting, from groups like databricks, tensorflow, etc? ** Consensus was in favor. === 9/8/2016 === ''Detailed minutes were not taken for this meeting'' * Clark Fitzgerald: internship report ** Developed two packages for low-level Spark integration: rddlist, sparklite ** Patched a bug in Spark ** ddR needs refactoring before Spark integration is feasible: *** dlist, dframe, and darray should be formal classes. *** Partitions of data should be represented by a distributed list abstraction, and most functions (e.g., dmapply) should be implemented on top of that list. * Javier: sparklyr update ** Preparing for CRAN release ** Mario: what happened to sparkapi? *** Javier: sparkapi has been merged into sparklyr in order to avoid overhead of maintaining two packages. ddR can do everything it needs with sparklyr. * Luke Tierney: Update on the low-level vector abstraction, which might support interfaces like ddR and sparklyr. ** Overall approach seems feasible, but still working out a few details. ** Will land in a branch soon. * Bernd Bischl: update on the batchtools package ** Successor to BatchJobs based on in-memory database === 8/11/2016 === ''Meeting was canceled due to lack of availability.'' === 7/14/2016 === * Introduced Clark who is the intern funded by R Consortium. Clark is a graduate student from UC Davis. He will work on ddR integration with Spark and improving the core ddR API as well such as adding a distributed apply() for matrices, split function, etc. * Bernd: Can I play around with ddR now? What backend should I use? How robust is the code? ** Clark: It's in good enough shape to be played around with. We will continue to improve it. Hopefully the spark integration will be done before the end of my internship in September. * Q: Is anyone working on using ddR to make ML scale better. ** Indrajit: We have kmeans, glm, etc. already in CRAN. ** Michael Kane: We are working on glmnet and other packages related to algorithm development. * Javier gave a demo of sparklyr and sparkapi. ** Motivation for the pacakage: The SparkR package overrides the dplyr interface. This is an issue for RStudio. SparkR is not a CRAN package which makes it difficult to add changes. dplyr is the most popular tool by RStudio and is broken on SparkR. ** Sparklyr provides a dplyr interface. It will also support ML like interfaces, such as consuming a ML model. ** Sparklyr does not currently support any distributed computing features. Instead we can recommend ddR as the distributed computing framework on top of sparkapi. We will put the code in CRAN in a couple of weeks. ** Simon: Can you talk more about the wrapper/low level API to work with Spark? *** Javier: The package underneath the cover is called "sparkapi" it is to be used by pacakge builders. "spark_context()" and "invoke()" are the functionality to call scala methods. It does not you to currently run R user defined functions. I am currently working on enabling that feature. Depending upon the interest in using ddR with sparkapi, I can spend more time to make sparkapi feature rich. ** Indrajit: What versions of Spark are supported *** Javier: Anything after 1.6 ** Bernd: How do you export data? *** Javier: We are using all the code from SparkR. So everything in SparkR should continue to work. We don't need to change SparkR. We just need to maintain compatibility. ** Bernd: What happens when the RDDs are very large? *** Javier: Spark will spill on disk. * Michael Kane: Presented examples that he implemented on ddR. ** Talked about how the different distributed packages compare to each other in terms of functionality. ** Michael K. looked at glm and truncated SVD on ddR. Was able to implement irls on ddR by implementing two distributed functions such as "cross". In truncated SVD only needed to overload two different distributed multiplications. ** Ran these algorithms on the 1000 genome dataset. ** Overall liked ddR since it was easy to implement the algorithms in the package. ** New ideas: *** Trying to separate the data layer from the execution layer *** Create an API that works on "chunks" (which is similar to the "parts" API in ddR). Would like to add these APIs to ddR. *** Indrajit: You should be able to get some of the chunk like features by using parts and dmapply. E.g., you can call dmapply to read 10 different files, which correspond 10 chunks now. These are however wrapped as a darray or dframe. But you can continue to work on the individual chunks by using parts(i). === 6/2/2016 === * Round table introduction * (Michael) Goals for the group: ** Make a common abstraction/interfaces to make it easier to work with distributed data and R ** Unify the interface ** Working group will run for a year. Get an API defined, get at least one open source reference implementations ** not everyone needs to work hands on. We will create smaller groups to focus on those aspects. ** We tried to get a diverse group of participants * Logistics: meet monthly, focus groups may meet more often * R Consoritum may be able to figure ways to fund smaller projects that come out of the working group * Michael Kane: Should we start with an inventory of what is available and people are using? ** Michael Lawrence: Yes, we should find the collection of tools as well as the use cases that are common. ** Joe: I will figure out a wiki space. * Javier: Who are the end users? Simon: Common layer needed to get algorithms working. We started from algos and tried to find the minimal common api. One of the goals is to make sure everyone is on the same page and not trying to create his/her own custom interface. * Javier: Should we try to get people with more algo expertise? * Joe: Simon do you have a stack diagram? * Simon: Can we get R Consortium to help write things up and draw things? * Next meeting: Javier is going to present SparkR next time. 4d09a7c767db252eba697474b5f360ec106065ad 56 55 2016-12-09T05:27:29Z Indrajit roy 14 /* 12/08/2016 */ wikitext text/x-wiki == Goals and Purpose == The Distributed Computing Working Group will endorse the design of a common abstraction for distributed data structures in R. We aim to have at least one open-source implementation, as well as a SQL implementation, released within a year of forming the group. == Members == * '''Michael Lawrence''' (Genentech) * '''Indrajit Roy''' (HP Enterprise) * ''Joe Rickert'' (ISC liason, RStudio) * Bernd Bischl (LMU) * Matt Dowle (H2O) * Mario Inchiosa (Microsoft) * Michael Kane (Yale) * Javier Luraschi (RStudio) * Edward Ma (HP Enterprise) * Luke Tierney (University of Iowa) * Simon Urbanek (AT&T) * Bryan Lewis (Paradigm4) * Hossein Falaki (databricks) == Milestones == === Achieved === * Adopt ddR as a prototype for a standard API for distributed computing in R === 2016 Internship === Clark Fitzgerald, a PhD student in the UC Davis Statistics department, worked on ddR and Spark integration. * Wrote [https://github.com/clarkfitzg/sparklite sparklite] and [https://github.com/clarkfitzg/rddlist rddlist] as minimal proof-of-concept R packages to connect and store general data on Spark. [https://docs.google.com/presentation/d/1WfUQ2ockNku90GWMXonEhUEcVOWcgBmWwt5uYSSBYPY/edit?usp=sharing slides] * [https://issues.apache.org/jira/browse/SPARK-16785 Patched SparkR] to allow user defined functions returning binary columns. This allows implementation of different data structures in SparkR. * Updated [https://github.com/vertica/ddR/wiki/Design design documents] with suggested changes to DDR's internal design and object oriented model. * Improved [https://github.com/vertica/ddR/pull/15 testing and ddR internals]. === Outstanding === * Agree on a final standard API for distributed computing in R * Implement at least one scalable backend based on an open-source technology like Spark, SQL, etc == Open Questions == * How can we address the needs of both the end user data scientists and the algorithm implementers? * How should we share data between R and a system like Spark? * Is there any way to unify SparkR and sparklyr? * Could we use the abstractions of tensorflow to partially or fully integrate with platforms like Spark? == Minutes == === 12/08/2016 === * Yuan Tang from RStudio was the presenter ** Michael and Indrajit will write a status report for the working group sometime in December or January ** Yuan gave an overview of TensorFlow ** JJ, Dirk and Yuan are working on R layer for TensorFlow ** TensorFlow is a platform for machine learning as well as other computations (even math proofs). ** It is GPU optimized and distributed. ** It is used in search, speech recognition, Google photos, etc. ** TensorFlow computations are directed graphs. odes are operations and edges are tensors. ** A lot of array, matrix, etc. operations are available ** Backend is mostly C++. Python front end exists. ** TensorFlow R is based on the python fronted ** In multi-device setting, TensorFlow figures out which devices to use and manages communication between devices. ** Computations are fault tolerant ** Yuan has previously worked on Scikit Flow which is now TF.Learn. It’s a easy transition for Scikit learn users. ** Yuan gave a brief overview of the python interface ** TensorFlow in R handles conversion between R and Python. Syntax is very similar to python API ** Future work: Adding more examples and tutorials, integration with Kubernetes/Marathon like framework. ** During the Q/A there were questions related to whether R kernels can be supported in TensorFlow, and whether R dataframes are a natural wrapper for TensorFlow objects. === 11/10/2016 === * SparkR slides were presented by Hossein Faliki and Shivaram from Databricks and UC Berkeley: ** SparkR was a prototype from AMPLab (2014). Initially it had the RDD API and was similar to PySpark API ** In 2015, the merge with upstream Spark, the decision was made to integrate with the Dataframe API, and hide the RDD API ** In 2016 more MLLib algorithms have been integrated and new APIs have been added. A CRAN package will be released soon ** Original SparkR architecture runs R on the master that communicates with the JVM processes in the driver. the driver sends commands to the worker JVM processes, and executes them as scala/java statements. ** The system can read distributed data inside the JVM from different sources such as S3, HDFS, etc. ** The driver has a socket based connection between SparkR and the RBackend. RBackend runs on the JVM, deserializes the R code, and converts the R statements into Java calls. ** collect() and createDataFrame() are used to move data between R and JVM processes. createDataFrame will convert your local R data into a JVM based distributed data frame. ** The API has IO, Caching, MLLib, and SQL related commands ** Since Spark 2.0, we can run R processes inside the JVM worker processes. There is no need to keep long running R processes. ** There are 3 UDF functions (1) lapply, runs function on different value of a list (2) dapply, runs function on each partition of a data frame. You have to careful about how data is partitioned, and (3) gapply, performs a grouping on different column names and then runs the function on each group. ** The new CRAN package install.spark() will automatically download and install Spark. Automated CRAN checks have been added to every commit to the code. Should be available with Spark 2.1.0 * Q/A ** Currently trying to get zero copy dataframe between python and Spark. Spark 2.0 has an off heap manager that uses Arrow. Once this feature is tested on the Python API, the next step will be integration R. ** Spark dataframes gain from plan optimizations. It is not SparkR specific. R UDFs are still treated as black boxes by the optimizer ** Spark doesn't directly support matrixes. There is no immediate intent to do so either. One can store an array or vector as a single column of a Spark dataframe. === 10/13/2016 === ''Detailed minutes were not taken for this meeting'' * Mario Inchiosa: Microsoft's perspective on distributed computing with R ** Microsoft R Server: abstractions and algorithms for distributed computation on top of open-source R ** Desired features of a distributed API like ddR: *** Supports PEMA (initialize, processData, updateResults, processResults) *** Cross-platform *** Fast runtime *** Supports algorithm writer and data scientist *** Comes with a comprehensive set of algorithms *** Easy deployment ** ddR is making good progress but does not yet meet those requirements * Indrajit: ddR progress report and next steps ** Recap of Clark's internship ** Next step: implement some of Clark's design suggestions: https://github.com/vertica/ddR/wiki/Design ** Spark integration will be based on sparklyr ** Should we limit Spark interaction to the DataFrame API or directly interact with RDDs? *** Consensus: will likely need flexibility of RDDs to implement everything we need, e.g., arrays and lists ** Clark and Javier raised concerns about the scalability of sharing data between R and Spark *** Michael: Spark is a platform in its own right, so interoperability is important, should figure something out *** Bryan Lewis: Why not use tensor abstraction from tensorflow? Spark supports tensorflow and an R interface is already in the works. ** Michael raised the issue of additional funding from the R Consortium to continue Clark's work *** Joe Rickert suggested that the working group develop one or more white papers summarizing the findings of the working group for presentation to the Infrastructure Steering Committee. *** Consensus was in favor of this, and several pointed out that the progress so far has been worthwhile, despite not meeting the specific goals laid out in the proposal. * Michael: do we want to invite some external speakers, one per meeting, from groups like databricks, tensorflow, etc? ** Consensus was in favor. === 9/8/2016 === ''Detailed minutes were not taken for this meeting'' * Clark Fitzgerald: internship report ** Developed two packages for low-level Spark integration: rddlist, sparklite ** Patched a bug in Spark ** ddR needs refactoring before Spark integration is feasible: *** dlist, dframe, and darray should be formal classes. *** Partitions of data should be represented by a distributed list abstraction, and most functions (e.g., dmapply) should be implemented on top of that list. * Javier: sparklyr update ** Preparing for CRAN release ** Mario: what happened to sparkapi? *** Javier: sparkapi has been merged into sparklyr in order to avoid overhead of maintaining two packages. ddR can do everything it needs with sparklyr. * Luke Tierney: Update on the low-level vector abstraction, which might support interfaces like ddR and sparklyr. ** Overall approach seems feasible, but still working out a few details. ** Will land in a branch soon. * Bernd Bischl: update on the batchtools package ** Successor to BatchJobs based on in-memory database === 8/11/2016 === ''Meeting was canceled due to lack of availability.'' === 7/14/2016 === * Introduced Clark who is the intern funded by R Consortium. Clark is a graduate student from UC Davis. He will work on ddR integration with Spark and improving the core ddR API as well such as adding a distributed apply() for matrices, split function, etc. * Bernd: Can I play around with ddR now? What backend should I use? How robust is the code? ** Clark: It's in good enough shape to be played around with. We will continue to improve it. Hopefully the spark integration will be done before the end of my internship in September. * Q: Is anyone working on using ddR to make ML scale better. ** Indrajit: We have kmeans, glm, etc. already in CRAN. ** Michael Kane: We are working on glmnet and other packages related to algorithm development. * Javier gave a demo of sparklyr and sparkapi. ** Motivation for the pacakage: The SparkR package overrides the dplyr interface. This is an issue for RStudio. SparkR is not a CRAN package which makes it difficult to add changes. dplyr is the most popular tool by RStudio and is broken on SparkR. ** Sparklyr provides a dplyr interface. It will also support ML like interfaces, such as consuming a ML model. ** Sparklyr does not currently support any distributed computing features. Instead we can recommend ddR as the distributed computing framework on top of sparkapi. We will put the code in CRAN in a couple of weeks. ** Simon: Can you talk more about the wrapper/low level API to work with Spark? *** Javier: The package underneath the cover is called "sparkapi" it is to be used by pacakge builders. "spark_context()" and "invoke()" are the functionality to call scala methods. It does not you to currently run R user defined functions. I am currently working on enabling that feature. Depending upon the interest in using ddR with sparkapi, I can spend more time to make sparkapi feature rich. ** Indrajit: What versions of Spark are supported *** Javier: Anything after 1.6 ** Bernd: How do you export data? *** Javier: We are using all the code from SparkR. So everything in SparkR should continue to work. We don't need to change SparkR. We just need to maintain compatibility. ** Bernd: What happens when the RDDs are very large? *** Javier: Spark will spill on disk. * Michael Kane: Presented examples that he implemented on ddR. ** Talked about how the different distributed packages compare to each other in terms of functionality. ** Michael K. looked at glm and truncated SVD on ddR. Was able to implement irls on ddR by implementing two distributed functions such as "cross". In truncated SVD only needed to overload two different distributed multiplications. ** Ran these algorithms on the 1000 genome dataset. ** Overall liked ddR since it was easy to implement the algorithms in the package. ** New ideas: *** Trying to separate the data layer from the execution layer *** Create an API that works on "chunks" (which is similar to the "parts" API in ddR). Would like to add these APIs to ddR. *** Indrajit: You should be able to get some of the chunk like features by using parts and dmapply. E.g., you can call dmapply to read 10 different files, which correspond 10 chunks now. These are however wrapped as a darray or dframe. But you can continue to work on the individual chunks by using parts(i). === 6/2/2016 === * Round table introduction * (Michael) Goals for the group: ** Make a common abstraction/interfaces to make it easier to work with distributed data and R ** Unify the interface ** Working group will run for a year. Get an API defined, get at least one open source reference implementations ** not everyone needs to work hands on. We will create smaller groups to focus on those aspects. ** We tried to get a diverse group of participants * Logistics: meet monthly, focus groups may meet more often * R Consoritum may be able to figure ways to fund smaller projects that come out of the working group * Michael Kane: Should we start with an inventory of what is available and people are using? ** Michael Lawrence: Yes, we should find the collection of tools as well as the use cases that are common. ** Joe: I will figure out a wiki space. * Javier: Who are the end users? Simon: Common layer needed to get algorithms working. We started from algos and tried to find the minimal common api. One of the goals is to make sure everyone is on the same page and not trying to create his/her own custom interface. * Javier: Should we try to get people with more algo expertise? * Joe: Simon do you have a stack diagram? * Simon: Can we get R Consortium to help write things up and draw things? * Next meeting: Javier is going to present SparkR next time. 41e820a895d372f0b168bc2a8a8479b3f18a5555 55 54 2016-12-09T05:26:16Z Indrajit roy 14 /* Minutes */ wikitext text/x-wiki == Goals and Purpose == The Distributed Computing Working Group will endorse the design of a common abstraction for distributed data structures in R. We aim to have at least one open-source implementation, as well as a SQL implementation, released within a year of forming the group. == Members == * '''Michael Lawrence''' (Genentech) * '''Indrajit Roy''' (HP Enterprise) * ''Joe Rickert'' (ISC liason, RStudio) * Bernd Bischl (LMU) * Matt Dowle (H2O) * Mario Inchiosa (Microsoft) * Michael Kane (Yale) * Javier Luraschi (RStudio) * Edward Ma (HP Enterprise) * Luke Tierney (University of Iowa) * Simon Urbanek (AT&T) * Bryan Lewis (Paradigm4) * Hossein Falaki (databricks) == Milestones == === Achieved === * Adopt ddR as a prototype for a standard API for distributed computing in R === 2016 Internship === Clark Fitzgerald, a PhD student in the UC Davis Statistics department, worked on ddR and Spark integration. * Wrote [https://github.com/clarkfitzg/sparklite sparklite] and [https://github.com/clarkfitzg/rddlist rddlist] as minimal proof-of-concept R packages to connect and store general data on Spark. [https://docs.google.com/presentation/d/1WfUQ2ockNku90GWMXonEhUEcVOWcgBmWwt5uYSSBYPY/edit?usp=sharing slides] * [https://issues.apache.org/jira/browse/SPARK-16785 Patched SparkR] to allow user defined functions returning binary columns. This allows implementation of different data structures in SparkR. * Updated [https://github.com/vertica/ddR/wiki/Design design documents] with suggested changes to DDR's internal design and object oriented model. * Improved [https://github.com/vertica/ddR/pull/15 testing and ddR internals]. === Outstanding === * Agree on a final standard API for distributed computing in R * Implement at least one scalable backend based on an open-source technology like Spark, SQL, etc == Open Questions == * How can we address the needs of both the end user data scientists and the algorithm implementers? * How should we share data between R and a system like Spark? * Is there any way to unify SparkR and sparklyr? * Could we use the abstractions of tensorflow to partially or fully integrate with platforms like Spark? == Minutes == === 12/08/2016 === * Yuan Tang from RStudio was the presenter ** Michael and Indrajit will write a status report for the working group sometime in December or January ** Yuan gave an overview of TensorFlow ** JJ, Dirk and Yuan are working on R layer for TensorFlow ** TensorFlow is a platform for machine learning as well as other computations (even math proofs). ** It is GPU optimized and distributed. ** It is used in search, speech recognition, Google photos, etc. ** TensorFlow computations are directed graphs. odes are operations and edges are tensors. ** A lot of array, matrix, etc. operations are available ** Backend is mostly C++. Python front end exists. ** TensorFlow R is based on the python fronted ** In multi-device setting, TensorFlow figures out which devices to use and manages communication between devices. ** Computations are fault tolerant ** Yuan has previously worked on Scikit Flow which is now TF.Learn. It’s a easy transition for Scikit learn users. ** Yuan gave a brief overview of the python interface ** TensorFlow in R handles conversion between R and Python. Syntax is very similar to python API ** Future work: Adding more examples and tutorials, integration with Kubernetes/Marathon like framework. === 11/10/2016 === * SparkR slides were presented by Hossein Faliki and Shivaram from Databricks and UC Berkeley: ** SparkR was a prototype from AMPLab (2014). Initially it had the RDD API and was similar to PySpark API ** In 2015, the merge with upstream Spark, the decision was made to integrate with the Dataframe API, and hide the RDD API ** In 2016 more MLLib algorithms have been integrated and new APIs have been added. A CRAN package will be released soon ** Original SparkR architecture runs R on the master that communicates with the JVM processes in the driver. the driver sends commands to the worker JVM processes, and executes them as scala/java statements. ** The system can read distributed data inside the JVM from different sources such as S3, HDFS, etc. ** The driver has a socket based connection between SparkR and the RBackend. RBackend runs on the JVM, deserializes the R code, and converts the R statements into Java calls. ** collect() and createDataFrame() are used to move data between R and JVM processes. createDataFrame will convert your local R data into a JVM based distributed data frame. ** The API has IO, Caching, MLLib, and SQL related commands ** Since Spark 2.0, we can run R processes inside the JVM worker processes. There is no need to keep long running R processes. ** There are 3 UDF functions (1) lapply, runs function on different value of a list (2) dapply, runs function on each partition of a data frame. You have to careful about how data is partitioned, and (3) gapply, performs a grouping on different column names and then runs the function on each group. ** The new CRAN package install.spark() will automatically download and install Spark. Automated CRAN checks have been added to every commit to the code. Should be available with Spark 2.1.0 * Q/A ** Currently trying to get zero copy dataframe between python and Spark. Spark 2.0 has an off heap manager that uses Arrow. Once this feature is tested on the Python API, the next step will be integration R. ** Spark dataframes gain from plan optimizations. It is not SparkR specific. R UDFs are still treated as black boxes by the optimizer ** Spark doesn't directly support matrixes. There is no immediate intent to do so either. One can store an array or vector as a single column of a Spark dataframe. === 10/13/2016 === ''Detailed minutes were not taken for this meeting'' * Mario Inchiosa: Microsoft's perspective on distributed computing with R ** Microsoft R Server: abstractions and algorithms for distributed computation on top of open-source R ** Desired features of a distributed API like ddR: *** Supports PEMA (initialize, processData, updateResults, processResults) *** Cross-platform *** Fast runtime *** Supports algorithm writer and data scientist *** Comes with a comprehensive set of algorithms *** Easy deployment ** ddR is making good progress but does not yet meet those requirements * Indrajit: ddR progress report and next steps ** Recap of Clark's internship ** Next step: implement some of Clark's design suggestions: https://github.com/vertica/ddR/wiki/Design ** Spark integration will be based on sparklyr ** Should we limit Spark interaction to the DataFrame API or directly interact with RDDs? *** Consensus: will likely need flexibility of RDDs to implement everything we need, e.g., arrays and lists ** Clark and Javier raised concerns about the scalability of sharing data between R and Spark *** Michael: Spark is a platform in its own right, so interoperability is important, should figure something out *** Bryan Lewis: Why not use tensor abstraction from tensorflow? Spark supports tensorflow and an R interface is already in the works. ** Michael raised the issue of additional funding from the R Consortium to continue Clark's work *** Joe Rickert suggested that the working group develop one or more white papers summarizing the findings of the working group for presentation to the Infrastructure Steering Committee. *** Consensus was in favor of this, and several pointed out that the progress so far has been worthwhile, despite not meeting the specific goals laid out in the proposal. * Michael: do we want to invite some external speakers, one per meeting, from groups like databricks, tensorflow, etc? ** Consensus was in favor. === 9/8/2016 === ''Detailed minutes were not taken for this meeting'' * Clark Fitzgerald: internship report ** Developed two packages for low-level Spark integration: rddlist, sparklite ** Patched a bug in Spark ** ddR needs refactoring before Spark integration is feasible: *** dlist, dframe, and darray should be formal classes. *** Partitions of data should be represented by a distributed list abstraction, and most functions (e.g., dmapply) should be implemented on top of that list. * Javier: sparklyr update ** Preparing for CRAN release ** Mario: what happened to sparkapi? *** Javier: sparkapi has been merged into sparklyr in order to avoid overhead of maintaining two packages. ddR can do everything it needs with sparklyr. * Luke Tierney: Update on the low-level vector abstraction, which might support interfaces like ddR and sparklyr. ** Overall approach seems feasible, but still working out a few details. ** Will land in a branch soon. * Bernd Bischl: update on the batchtools package ** Successor to BatchJobs based on in-memory database === 8/11/2016 === ''Meeting was canceled due to lack of availability.'' === 7/14/2016 === * Introduced Clark who is the intern funded by R Consortium. Clark is a graduate student from UC Davis. He will work on ddR integration with Spark and improving the core ddR API as well such as adding a distributed apply() for matrices, split function, etc. * Bernd: Can I play around with ddR now? What backend should I use? How robust is the code? ** Clark: It's in good enough shape to be played around with. We will continue to improve it. Hopefully the spark integration will be done before the end of my internship in September. * Q: Is anyone working on using ddR to make ML scale better. ** Indrajit: We have kmeans, glm, etc. already in CRAN. ** Michael Kane: We are working on glmnet and other packages related to algorithm development. * Javier gave a demo of sparklyr and sparkapi. ** Motivation for the pacakage: The SparkR package overrides the dplyr interface. This is an issue for RStudio. SparkR is not a CRAN package which makes it difficult to add changes. dplyr is the most popular tool by RStudio and is broken on SparkR. ** Sparklyr provides a dplyr interface. It will also support ML like interfaces, such as consuming a ML model. ** Sparklyr does not currently support any distributed computing features. Instead we can recommend ddR as the distributed computing framework on top of sparkapi. We will put the code in CRAN in a couple of weeks. ** Simon: Can you talk more about the wrapper/low level API to work with Spark? *** Javier: The package underneath the cover is called "sparkapi" it is to be used by pacakge builders. "spark_context()" and "invoke()" are the functionality to call scala methods. It does not you to currently run R user defined functions. I am currently working on enabling that feature. Depending upon the interest in using ddR with sparkapi, I can spend more time to make sparkapi feature rich. ** Indrajit: What versions of Spark are supported *** Javier: Anything after 1.6 ** Bernd: How do you export data? *** Javier: We are using all the code from SparkR. So everything in SparkR should continue to work. We don't need to change SparkR. We just need to maintain compatibility. ** Bernd: What happens when the RDDs are very large? *** Javier: Spark will spill on disk. * Michael Kane: Presented examples that he implemented on ddR. ** Talked about how the different distributed packages compare to each other in terms of functionality. ** Michael K. looked at glm and truncated SVD on ddR. Was able to implement irls on ddR by implementing two distributed functions such as "cross". In truncated SVD only needed to overload two different distributed multiplications. ** Ran these algorithms on the 1000 genome dataset. ** Overall liked ddR since it was easy to implement the algorithms in the package. ** New ideas: *** Trying to separate the data layer from the execution layer *** Create an API that works on "chunks" (which is similar to the "parts" API in ddR). Would like to add these APIs to ddR. *** Indrajit: You should be able to get some of the chunk like features by using parts and dmapply. E.g., you can call dmapply to read 10 different files, which correspond 10 chunks now. These are however wrapped as a darray or dframe. But you can continue to work on the individual chunks by using parts(i). === 6/2/2016 === * Round table introduction * (Michael) Goals for the group: ** Make a common abstraction/interfaces to make it easier to work with distributed data and R ** Unify the interface ** Working group will run for a year. Get an API defined, get at least one open source reference implementations ** not everyone needs to work hands on. We will create smaller groups to focus on those aspects. ** We tried to get a diverse group of participants * Logistics: meet monthly, focus groups may meet more often * R Consoritum may be able to figure ways to fund smaller projects that come out of the working group * Michael Kane: Should we start with an inventory of what is available and people are using? ** Michael Lawrence: Yes, we should find the collection of tools as well as the use cases that are common. ** Joe: I will figure out a wiki space. * Javier: Who are the end users? Simon: Common layer needed to get algorithms working. We started from algos and tried to find the minimal common api. One of the goals is to make sure everyone is on the same page and not trying to create his/her own custom interface. * Javier: Should we try to get people with more algo expertise? * Joe: Simon do you have a stack diagram? * Simon: Can we get R Consortium to help write things up and draw things? * Next meeting: Javier is going to present SparkR next time. e1eafae30b37ee2d2067b37ef1ed73d52b97ecf1 54 53 2016-11-10T22:42:50Z Indrajit roy 14 /* 11/10/2016 */ wikitext text/x-wiki == Goals and Purpose == The Distributed Computing Working Group will endorse the design of a common abstraction for distributed data structures in R. We aim to have at least one open-source implementation, as well as a SQL implementation, released within a year of forming the group. == Members == * '''Michael Lawrence''' (Genentech) * '''Indrajit Roy''' (HP Enterprise) * ''Joe Rickert'' (ISC liason, RStudio) * Bernd Bischl (LMU) * Matt Dowle (H2O) * Mario Inchiosa (Microsoft) * Michael Kane (Yale) * Javier Luraschi (RStudio) * Edward Ma (HP Enterprise) * Luke Tierney (University of Iowa) * Simon Urbanek (AT&T) * Bryan Lewis (Paradigm4) * Hossein Falaki (databricks) == Milestones == === Achieved === * Adopt ddR as a prototype for a standard API for distributed computing in R === 2016 Internship === Clark Fitzgerald, a PhD student in the UC Davis Statistics department, worked on ddR and Spark integration. * Wrote [https://github.com/clarkfitzg/sparklite sparklite] and [https://github.com/clarkfitzg/rddlist rddlist] as minimal proof-of-concept R packages to connect and store general data on Spark. [https://docs.google.com/presentation/d/1WfUQ2ockNku90GWMXonEhUEcVOWcgBmWwt5uYSSBYPY/edit?usp=sharing slides] * [https://issues.apache.org/jira/browse/SPARK-16785 Patched SparkR] to allow user defined functions returning binary columns. This allows implementation of different data structures in SparkR. * Updated [https://github.com/vertica/ddR/wiki/Design design documents] with suggested changes to DDR's internal design and object oriented model. * Improved [https://github.com/vertica/ddR/pull/15 testing and ddR internals]. === Outstanding === * Agree on a final standard API for distributed computing in R * Implement at least one scalable backend based on an open-source technology like Spark, SQL, etc == Open Questions == * How can we address the needs of both the end user data scientists and the algorithm implementers? * How should we share data between R and a system like Spark? * Is there any way to unify SparkR and sparklyr? * Could we use the abstractions of tensorflow to partially or fully integrate with platforms like Spark? == Minutes == === 11/10/2016 === * SparkR slides were presented by Hossein Faliki and Shivaram from Databricks and UC Berkeley: ** SparkR was a prototype from AMPLab (2014). Initially it had the RDD API and was similar to PySpark API ** In 2015, the merge with upstream Spark, the decision was made to integrate with the Dataframe API, and hide the RDD API ** In 2016 more MLLib algorithms have been integrated and new APIs have been added. A CRAN package will be released soon ** Original SparkR architecture runs R on the master that communicates with the JVM processes in the driver. the driver sends commands to the worker JVM processes, and executes them as scala/java statements. ** The system can read distributed data inside the JVM from different sources such as S3, HDFS, etc. ** The driver has a socket based connection between SparkR and the RBackend. RBackend runs on the JVM, deserializes the R code, and converts the R statements into Java calls. ** collect() and createDataFrame() are used to move data between R and JVM processes. createDataFrame will convert your local R data into a JVM based distributed data frame. ** The API has IO, Caching, MLLib, and SQL related commands ** Since Spark 2.0, we can run R processes inside the JVM worker processes. There is no need to keep long running R processes. ** There are 3 UDF functions (1) lapply, runs function on different value of a list (2) dapply, runs function on each partition of a data frame. You have to careful about how data is partitioned, and (3) gapply, performs a grouping on different column names and then runs the function on each group. ** The new CRAN package install.spark() will automatically download and install Spark. Automated CRAN checks have been added to every commit to the code. Should be available with Spark 2.1.0 * Q/A ** Currently trying to get zero copy dataframe between python and Spark. Spark 2.0 has an off heap manager that uses Arrow. Once this feature is tested on the Python API, the next step will be integration R. ** Spark dataframes gain from plan optimizations. It is not SparkR specific. R UDFs are still treated as black boxes by the optimizer ** Spark doesn't directly support matrixes. There is no immediate intent to do so either. One can store an array or vector as a single column of a Spark dataframe. === 10/13/2016 === ''Detailed minutes were not taken for this meeting'' * Mario Inchiosa: Microsoft's perspective on distributed computing with R ** Microsoft R Server: abstractions and algorithms for distributed computation on top of open-source R ** Desired features of a distributed API like ddR: *** Supports PEMA (initialize, processData, updateResults, processResults) *** Cross-platform *** Fast runtime *** Supports algorithm writer and data scientist *** Comes with a comprehensive set of algorithms *** Easy deployment ** ddR is making good progress but does not yet meet those requirements * Indrajit: ddR progress report and next steps ** Recap of Clark's internship ** Next step: implement some of Clark's design suggestions: https://github.com/vertica/ddR/wiki/Design ** Spark integration will be based on sparklyr ** Should we limit Spark interaction to the DataFrame API or directly interact with RDDs? *** Consensus: will likely need flexibility of RDDs to implement everything we need, e.g., arrays and lists ** Clark and Javier raised concerns about the scalability of sharing data between R and Spark *** Michael: Spark is a platform in its own right, so interoperability is important, should figure something out *** Bryan Lewis: Why not use tensor abstraction from tensorflow? Spark supports tensorflow and an R interface is already in the works. ** Michael raised the issue of additional funding from the R Consortium to continue Clark's work *** Joe Rickert suggested that the working group develop one or more white papers summarizing the findings of the working group for presentation to the Infrastructure Steering Committee. *** Consensus was in favor of this, and several pointed out that the progress so far has been worthwhile, despite not meeting the specific goals laid out in the proposal. * Michael: do we want to invite some external speakers, one per meeting, from groups like databricks, tensorflow, etc? ** Consensus was in favor. === 9/8/2016 === ''Detailed minutes were not taken for this meeting'' * Clark Fitzgerald: internship report ** Developed two packages for low-level Spark integration: rddlist, sparklite ** Patched a bug in Spark ** ddR needs refactoring before Spark integration is feasible: *** dlist, dframe, and darray should be formal classes. *** Partitions of data should be represented by a distributed list abstraction, and most functions (e.g., dmapply) should be implemented on top of that list. * Javier: sparklyr update ** Preparing for CRAN release ** Mario: what happened to sparkapi? *** Javier: sparkapi has been merged into sparklyr in order to avoid overhead of maintaining two packages. ddR can do everything it needs with sparklyr. * Luke Tierney: Update on the low-level vector abstraction, which might support interfaces like ddR and sparklyr. ** Overall approach seems feasible, but still working out a few details. ** Will land in a branch soon. * Bernd Bischl: update on the batchtools package ** Successor to BatchJobs based on in-memory database === 8/11/2016 === ''Meeting was canceled due to lack of availability.'' === 7/14/2016 === * Introduced Clark who is the intern funded by R Consortium. Clark is a graduate student from UC Davis. He will work on ddR integration with Spark and improving the core ddR API as well such as adding a distributed apply() for matrices, split function, etc. * Bernd: Can I play around with ddR now? What backend should I use? How robust is the code? ** Clark: It's in good enough shape to be played around with. We will continue to improve it. Hopefully the spark integration will be done before the end of my internship in September. * Q: Is anyone working on using ddR to make ML scale better. ** Indrajit: We have kmeans, glm, etc. already in CRAN. ** Michael Kane: We are working on glmnet and other packages related to algorithm development. * Javier gave a demo of sparklyr and sparkapi. ** Motivation for the pacakage: The SparkR package overrides the dplyr interface. This is an issue for RStudio. SparkR is not a CRAN package which makes it difficult to add changes. dplyr is the most popular tool by RStudio and is broken on SparkR. ** Sparklyr provides a dplyr interface. It will also support ML like interfaces, such as consuming a ML model. ** Sparklyr does not currently support any distributed computing features. Instead we can recommend ddR as the distributed computing framework on top of sparkapi. We will put the code in CRAN in a couple of weeks. ** Simon: Can you talk more about the wrapper/low level API to work with Spark? *** Javier: The package underneath the cover is called "sparkapi" it is to be used by pacakge builders. "spark_context()" and "invoke()" are the functionality to call scala methods. It does not you to currently run R user defined functions. I am currently working on enabling that feature. Depending upon the interest in using ddR with sparkapi, I can spend more time to make sparkapi feature rich. ** Indrajit: What versions of Spark are supported *** Javier: Anything after 1.6 ** Bernd: How do you export data? *** Javier: We are using all the code from SparkR. So everything in SparkR should continue to work. We don't need to change SparkR. We just need to maintain compatibility. ** Bernd: What happens when the RDDs are very large? *** Javier: Spark will spill on disk. * Michael Kane: Presented examples that he implemented on ddR. ** Talked about how the different distributed packages compare to each other in terms of functionality. ** Michael K. looked at glm and truncated SVD on ddR. Was able to implement irls on ddR by implementing two distributed functions such as "cross". In truncated SVD only needed to overload two different distributed multiplications. ** Ran these algorithms on the 1000 genome dataset. ** Overall liked ddR since it was easy to implement the algorithms in the package. ** New ideas: *** Trying to separate the data layer from the execution layer *** Create an API that works on "chunks" (which is similar to the "parts" API in ddR). Would like to add these APIs to ddR. *** Indrajit: You should be able to get some of the chunk like features by using parts and dmapply. E.g., you can call dmapply to read 10 different files, which correspond 10 chunks now. These are however wrapped as a darray or dframe. But you can continue to work on the individual chunks by using parts(i). === 6/2/2016 === * Round table introduction * (Michael) Goals for the group: ** Make a common abstraction/interfaces to make it easier to work with distributed data and R ** Unify the interface ** Working group will run for a year. Get an API defined, get at least one open source reference implementations ** not everyone needs to work hands on. We will create smaller groups to focus on those aspects. ** We tried to get a diverse group of participants * Logistics: meet monthly, focus groups may meet more often * R Consoritum may be able to figure ways to fund smaller projects that come out of the working group * Michael Kane: Should we start with an inventory of what is available and people are using? ** Michael Lawrence: Yes, we should find the collection of tools as well as the use cases that are common. ** Joe: I will figure out a wiki space. * Javier: Who are the end users? Simon: Common layer needed to get algorithms working. We started from algos and tried to find the minimal common api. One of the goals is to make sure everyone is on the same page and not trying to create his/her own custom interface. * Javier: Should we try to get people with more algo expertise? * Joe: Simon do you have a stack diagram? * Simon: Can we get R Consortium to help write things up and draw things? * Next meeting: Javier is going to present SparkR next time. a7147f8c97dbe66af90d6e9b40407a24badc6893 53 52 2016-11-10T22:42:19Z Indrajit roy 14 /* 11/10/2016 */ wikitext text/x-wiki == Goals and Purpose == The Distributed Computing Working Group will endorse the design of a common abstraction for distributed data structures in R. We aim to have at least one open-source implementation, as well as a SQL implementation, released within a year of forming the group. == Members == * '''Michael Lawrence''' (Genentech) * '''Indrajit Roy''' (HP Enterprise) * ''Joe Rickert'' (ISC liason, RStudio) * Bernd Bischl (LMU) * Matt Dowle (H2O) * Mario Inchiosa (Microsoft) * Michael Kane (Yale) * Javier Luraschi (RStudio) * Edward Ma (HP Enterprise) * Luke Tierney (University of Iowa) * Simon Urbanek (AT&T) * Bryan Lewis (Paradigm4) * Hossein Falaki (databricks) == Milestones == === Achieved === * Adopt ddR as a prototype for a standard API for distributed computing in R === 2016 Internship === Clark Fitzgerald, a PhD student in the UC Davis Statistics department, worked on ddR and Spark integration. * Wrote [https://github.com/clarkfitzg/sparklite sparklite] and [https://github.com/clarkfitzg/rddlist rddlist] as minimal proof-of-concept R packages to connect and store general data on Spark. [https://docs.google.com/presentation/d/1WfUQ2ockNku90GWMXonEhUEcVOWcgBmWwt5uYSSBYPY/edit?usp=sharing slides] * [https://issues.apache.org/jira/browse/SPARK-16785 Patched SparkR] to allow user defined functions returning binary columns. This allows implementation of different data structures in SparkR. * Updated [https://github.com/vertica/ddR/wiki/Design design documents] with suggested changes to DDR's internal design and object oriented model. * Improved [https://github.com/vertica/ddR/pull/15 testing and ddR internals]. === Outstanding === * Agree on a final standard API for distributed computing in R * Implement at least one scalable backend based on an open-source technology like Spark, SQL, etc == Open Questions == * How can we address the needs of both the end user data scientists and the algorithm implementers? * How should we share data between R and a system like Spark? * Is there any way to unify SparkR and sparklyr? * Could we use the abstractions of tensorflow to partially or fully integrate with platforms like Spark? == Minutes == === 11/10/2016 === * SparkR slides were presented by Hossein Faliki and Shivaram from Databricks and UC Berkeley: ** SparkR was a prototype from AMPLab (2014). Initially it had the RDD API and was similar to PySpark API ** In 2015, the merge with upstream Spark, the decision was made to integrate with the Dataframe API, and hide the RDD API ** In 2016 more MLLib algorithms have been integrated and new APIs have been added. A CRAN package will be released soon ** Original SparkR architecture runs R on the master that communicates with the JVM processes in the driver. the driver sends commands to the worker JVM processes, and executes them as scala/java statements. ** The system can read distributed data inside the JVM from different sources such as S3, HDFS, etc. ** The driver has a socket based connection between SparkR and the RBackend. RBackend runs on the JVM, deserializes the R code, and converts the R statements into Java calls. ** collect() and createDataFrame() are used to move data between R and JVM processes. createDataFrame will convert your local R data into a JVM based distributed data frame. ** The API has IO, Caching, MLLib, and SQL related commands ** Since Spark 2.0, we can run R processes inside the JVM worker processes. There is no need to keep long running R processes. ** There are 3 UDF functions (1) lapply, runs function on different value of a list (2) dapply, runs function on each partition of a data frame. You have to careful about how data is partitioned, and (3) gapply, performs a grouping on different column names and then runs the function on each group. ** The new CRAN package install.spark() will automatically download and install Spark. Automated CRAN checks have been added to every commit to the code. Should be available with Spark 2.1.0 * Q/A ** Currently trying to get zero copy dataframe between python and Spark. Spark 2.0 has an off heap manager that uses Arrow. Once this feature is tested on the Python API, the next step will be integration R. ** Spark dataframes gain from plan optimizations. It is not SparkR specific. R UDFs are still treated as black boxes by the optimizer ** Spark doesn't directly support matrixes. There is no immediate intent to do so either. One can store an array or vector as a single column of a Spark dataframe. === 10/13/2016 === ''Detailed minutes were not taken for this meeting'' * Mario Inchiosa: Microsoft's perspective on distributed computing with R ** Microsoft R Server: abstractions and algorithms for distributed computation on top of open-source R ** Desired features of a distributed API like ddR: *** Supports PEMA (initialize, processData, updateResults, processResults) *** Cross-platform *** Fast runtime *** Supports algorithm writer and data scientist *** Comes with a comprehensive set of algorithms *** Easy deployment ** ddR is making good progress but does not yet meet those requirements * Indrajit: ddR progress report and next steps ** Recap of Clark's internship ** Next step: implement some of Clark's design suggestions: https://github.com/vertica/ddR/wiki/Design ** Spark integration will be based on sparklyr ** Should we limit Spark interaction to the DataFrame API or directly interact with RDDs? *** Consensus: will likely need flexibility of RDDs to implement everything we need, e.g., arrays and lists ** Clark and Javier raised concerns about the scalability of sharing data between R and Spark *** Michael: Spark is a platform in its own right, so interoperability is important, should figure something out *** Bryan Lewis: Why not use tensor abstraction from tensorflow? Spark supports tensorflow and an R interface is already in the works. ** Michael raised the issue of additional funding from the R Consortium to continue Clark's work *** Joe Rickert suggested that the working group develop one or more white papers summarizing the findings of the working group for presentation to the Infrastructure Steering Committee. *** Consensus was in favor of this, and several pointed out that the progress so far has been worthwhile, despite not meeting the specific goals laid out in the proposal. * Michael: do we want to invite some external speakers, one per meeting, from groups like databricks, tensorflow, etc? ** Consensus was in favor. === 9/8/2016 === ''Detailed minutes were not taken for this meeting'' * Clark Fitzgerald: internship report ** Developed two packages for low-level Spark integration: rddlist, sparklite ** Patched a bug in Spark ** ddR needs refactoring before Spark integration is feasible: *** dlist, dframe, and darray should be formal classes. *** Partitions of data should be represented by a distributed list abstraction, and most functions (e.g., dmapply) should be implemented on top of that list. * Javier: sparklyr update ** Preparing for CRAN release ** Mario: what happened to sparkapi? *** Javier: sparkapi has been merged into sparklyr in order to avoid overhead of maintaining two packages. ddR can do everything it needs with sparklyr. * Luke Tierney: Update on the low-level vector abstraction, which might support interfaces like ddR and sparklyr. ** Overall approach seems feasible, but still working out a few details. ** Will land in a branch soon. * Bernd Bischl: update on the batchtools package ** Successor to BatchJobs based on in-memory database === 8/11/2016 === ''Meeting was canceled due to lack of availability.'' === 7/14/2016 === * Introduced Clark who is the intern funded by R Consortium. Clark is a graduate student from UC Davis. He will work on ddR integration with Spark and improving the core ddR API as well such as adding a distributed apply() for matrices, split function, etc. * Bernd: Can I play around with ddR now? What backend should I use? How robust is the code? ** Clark: It's in good enough shape to be played around with. We will continue to improve it. Hopefully the spark integration will be done before the end of my internship in September. * Q: Is anyone working on using ddR to make ML scale better. ** Indrajit: We have kmeans, glm, etc. already in CRAN. ** Michael Kane: We are working on glmnet and other packages related to algorithm development. * Javier gave a demo of sparklyr and sparkapi. ** Motivation for the pacakage: The SparkR package overrides the dplyr interface. This is an issue for RStudio. SparkR is not a CRAN package which makes it difficult to add changes. dplyr is the most popular tool by RStudio and is broken on SparkR. ** Sparklyr provides a dplyr interface. It will also support ML like interfaces, such as consuming a ML model. ** Sparklyr does not currently support any distributed computing features. Instead we can recommend ddR as the distributed computing framework on top of sparkapi. We will put the code in CRAN in a couple of weeks. ** Simon: Can you talk more about the wrapper/low level API to work with Spark? *** Javier: The package underneath the cover is called "sparkapi" it is to be used by pacakge builders. "spark_context()" and "invoke()" are the functionality to call scala methods. It does not you to currently run R user defined functions. I am currently working on enabling that feature. Depending upon the interest in using ddR with sparkapi, I can spend more time to make sparkapi feature rich. ** Indrajit: What versions of Spark are supported *** Javier: Anything after 1.6 ** Bernd: How do you export data? *** Javier: We are using all the code from SparkR. So everything in SparkR should continue to work. We don't need to change SparkR. We just need to maintain compatibility. ** Bernd: What happens when the RDDs are very large? *** Javier: Spark will spill on disk. * Michael Kane: Presented examples that he implemented on ddR. ** Talked about how the different distributed packages compare to each other in terms of functionality. ** Michael K. looked at glm and truncated SVD on ddR. Was able to implement irls on ddR by implementing two distributed functions such as "cross". In truncated SVD only needed to overload two different distributed multiplications. ** Ran these algorithms on the 1000 genome dataset. ** Overall liked ddR since it was easy to implement the algorithms in the package. ** New ideas: *** Trying to separate the data layer from the execution layer *** Create an API that works on "chunks" (which is similar to the "parts" API in ddR). Would like to add these APIs to ddR. *** Indrajit: You should be able to get some of the chunk like features by using parts and dmapply. E.g., you can call dmapply to read 10 different files, which correspond 10 chunks now. These are however wrapped as a darray or dframe. But you can continue to work on the individual chunks by using parts(i). === 6/2/2016 === * Round table introduction * (Michael) Goals for the group: ** Make a common abstraction/interfaces to make it easier to work with distributed data and R ** Unify the interface ** Working group will run for a year. Get an API defined, get at least one open source reference implementations ** not everyone needs to work hands on. We will create smaller groups to focus on those aspects. ** We tried to get a diverse group of participants * Logistics: meet monthly, focus groups may meet more often * R Consoritum may be able to figure ways to fund smaller projects that come out of the working group * Michael Kane: Should we start with an inventory of what is available and people are using? ** Michael Lawrence: Yes, we should find the collection of tools as well as the use cases that are common. ** Joe: I will figure out a wiki space. * Javier: Who are the end users? Simon: Common layer needed to get algorithms working. We started from algos and tried to find the minimal common api. One of the goals is to make sure everyone is on the same page and not trying to create his/her own custom interface. * Javier: Should we try to get people with more algo expertise? * Joe: Simon do you have a stack diagram? * Simon: Can we get R Consortium to help write things up and draw things? * Next meeting: Javier is going to present SparkR next time. 97f7ba62f54e3eddea96e80da2235f04076c3bf6 52 51 2016-11-10T22:33:28Z Indrajit roy 14 /* Minutes */ wikitext text/x-wiki == Goals and Purpose == The Distributed Computing Working Group will endorse the design of a common abstraction for distributed data structures in R. We aim to have at least one open-source implementation, as well as a SQL implementation, released within a year of forming the group. == Members == * '''Michael Lawrence''' (Genentech) * '''Indrajit Roy''' (HP Enterprise) * ''Joe Rickert'' (ISC liason, RStudio) * Bernd Bischl (LMU) * Matt Dowle (H2O) * Mario Inchiosa (Microsoft) * Michael Kane (Yale) * Javier Luraschi (RStudio) * Edward Ma (HP Enterprise) * Luke Tierney (University of Iowa) * Simon Urbanek (AT&T) * Bryan Lewis (Paradigm4) * Hossein Falaki (databricks) == Milestones == === Achieved === * Adopt ddR as a prototype for a standard API for distributed computing in R === 2016 Internship === Clark Fitzgerald, a PhD student in the UC Davis Statistics department, worked on ddR and Spark integration. * Wrote [https://github.com/clarkfitzg/sparklite sparklite] and [https://github.com/clarkfitzg/rddlist rddlist] as minimal proof-of-concept R packages to connect and store general data on Spark. [https://docs.google.com/presentation/d/1WfUQ2ockNku90GWMXonEhUEcVOWcgBmWwt5uYSSBYPY/edit?usp=sharing slides] * [https://issues.apache.org/jira/browse/SPARK-16785 Patched SparkR] to allow user defined functions returning binary columns. This allows implementation of different data structures in SparkR. * Updated [https://github.com/vertica/ddR/wiki/Design design documents] with suggested changes to DDR's internal design and object oriented model. * Improved [https://github.com/vertica/ddR/pull/15 testing and ddR internals]. === Outstanding === * Agree on a final standard API for distributed computing in R * Implement at least one scalable backend based on an open-source technology like Spark, SQL, etc == Open Questions == * How can we address the needs of both the end user data scientists and the algorithm implementers? * How should we share data between R and a system like Spark? * Is there any way to unify SparkR and sparklyr? * Could we use the abstractions of tensorflow to partially or fully integrate with platforms like Spark? == Minutes == === 11/10/2016 === * Slides were presented by Hossein Faliki and Shivaram from Databricks and UC Berkeley: ** SparkR was a prototype was from AMPLab in 2014. Initially it had the RDD API and was similar to PySpark API ** In 2015 the merge with upstream Spark, the decision was made to integrate with the Dataframe API, and hide the RDD API ** In 2016 onwards more MLLib algos have been exposed and new APIs have been added, and a CRAN package will be released soon ** SparkR architecture runs R on the Spark driver that communicates with the JVM processes in the driver, which are sent to the worker JVM processes, and executed as scala/java on workers. ** You can read distributed data inside the JVM from different sources such as S3, HDFS, etc. ** In the driver there is a socket based connection between SparkR and the RBackend which is on the JVM. RBackend deserializes the R code and converts them into Java calls. ** collect() and createDataFrame() are used to move data between R and JVM across sockets. createDataFrame will convert your local R data into a JVM based distributed data frame. ** The API has IO, Caching, MLLib, and SQL commands ** Since Spark 2.0, we can run R processes inside the JVM worker processes. There is no need to keep long running R processes. ** There are 3 UDF functions (1) lapply, run function on different value of a list (2) dapply, run function on each partition of a data frame. You have to careful about how data is partitioned (3) gapply, performs a grouping on differnt column names and then runs the function on each group. ** New CRAN package. install.spark() to automatically download and install Spark. Automated CRAN checks with every commit to the code. Should be available with Spark 2.1.0 ** With Spark 2.0, Spark has a its off head manager where they will use Arrow. Once this is tested it on the Python integration we can use it for R. Currently trying to get zero copy dataframe between python and Spark. * Q/A ** Spark dataframes gain from plan optimizations. It is not SparkR specific. R UDFs are still treated as black boxes by the optimizer ** Spark doesn't directly support matrixes. There is no immediate intent to do so either. One can store an array or vector as a single column of a Spark dataframe. === 10/13/2016 === ''Detailed minutes were not taken for this meeting'' * Mario Inchiosa: Microsoft's perspective on distributed computing with R ** Microsoft R Server: abstractions and algorithms for distributed computation on top of open-source R ** Desired features of a distributed API like ddR: *** Supports PEMA (initialize, processData, updateResults, processResults) *** Cross-platform *** Fast runtime *** Supports algorithm writer and data scientist *** Comes with a comprehensive set of algorithms *** Easy deployment ** ddR is making good progress but does not yet meet those requirements * Indrajit: ddR progress report and next steps ** Recap of Clark's internship ** Next step: implement some of Clark's design suggestions: https://github.com/vertica/ddR/wiki/Design ** Spark integration will be based on sparklyr ** Should we limit Spark interaction to the DataFrame API or directly interact with RDDs? *** Consensus: will likely need flexibility of RDDs to implement everything we need, e.g., arrays and lists ** Clark and Javier raised concerns about the scalability of sharing data between R and Spark *** Michael: Spark is a platform in its own right, so interoperability is important, should figure something out *** Bryan Lewis: Why not use tensor abstraction from tensorflow? Spark supports tensorflow and an R interface is already in the works. ** Michael raised the issue of additional funding from the R Consortium to continue Clark's work *** Joe Rickert suggested that the working group develop one or more white papers summarizing the findings of the working group for presentation to the Infrastructure Steering Committee. *** Consensus was in favor of this, and several pointed out that the progress so far has been worthwhile, despite not meeting the specific goals laid out in the proposal. * Michael: do we want to invite some external speakers, one per meeting, from groups like databricks, tensorflow, etc? ** Consensus was in favor. === 9/8/2016 === ''Detailed minutes were not taken for this meeting'' * Clark Fitzgerald: internship report ** Developed two packages for low-level Spark integration: rddlist, sparklite ** Patched a bug in Spark ** ddR needs refactoring before Spark integration is feasible: *** dlist, dframe, and darray should be formal classes. *** Partitions of data should be represented by a distributed list abstraction, and most functions (e.g., dmapply) should be implemented on top of that list. * Javier: sparklyr update ** Preparing for CRAN release ** Mario: what happened to sparkapi? *** Javier: sparkapi has been merged into sparklyr in order to avoid overhead of maintaining two packages. ddR can do everything it needs with sparklyr. * Luke Tierney: Update on the low-level vector abstraction, which might support interfaces like ddR and sparklyr. ** Overall approach seems feasible, but still working out a few details. ** Will land in a branch soon. * Bernd Bischl: update on the batchtools package ** Successor to BatchJobs based on in-memory database === 8/11/2016 === ''Meeting was canceled due to lack of availability.'' === 7/14/2016 === * Introduced Clark who is the intern funded by R Consortium. Clark is a graduate student from UC Davis. He will work on ddR integration with Spark and improving the core ddR API as well such as adding a distributed apply() for matrices, split function, etc. * Bernd: Can I play around with ddR now? What backend should I use? How robust is the code? ** Clark: It's in good enough shape to be played around with. We will continue to improve it. Hopefully the spark integration will be done before the end of my internship in September. * Q: Is anyone working on using ddR to make ML scale better. ** Indrajit: We have kmeans, glm, etc. already in CRAN. ** Michael Kane: We are working on glmnet and other packages related to algorithm development. * Javier gave a demo of sparklyr and sparkapi. ** Motivation for the pacakage: The SparkR package overrides the dplyr interface. This is an issue for RStudio. SparkR is not a CRAN package which makes it difficult to add changes. dplyr is the most popular tool by RStudio and is broken on SparkR. ** Sparklyr provides a dplyr interface. It will also support ML like interfaces, such as consuming a ML model. ** Sparklyr does not currently support any distributed computing features. Instead we can recommend ddR as the distributed computing framework on top of sparkapi. We will put the code in CRAN in a couple of weeks. ** Simon: Can you talk more about the wrapper/low level API to work with Spark? *** Javier: The package underneath the cover is called "sparkapi" it is to be used by pacakge builders. "spark_context()" and "invoke()" are the functionality to call scala methods. It does not you to currently run R user defined functions. I am currently working on enabling that feature. Depending upon the interest in using ddR with sparkapi, I can spend more time to make sparkapi feature rich. ** Indrajit: What versions of Spark are supported *** Javier: Anything after 1.6 ** Bernd: How do you export data? *** Javier: We are using all the code from SparkR. So everything in SparkR should continue to work. We don't need to change SparkR. We just need to maintain compatibility. ** Bernd: What happens when the RDDs are very large? *** Javier: Spark will spill on disk. * Michael Kane: Presented examples that he implemented on ddR. ** Talked about how the different distributed packages compare to each other in terms of functionality. ** Michael K. looked at glm and truncated SVD on ddR. Was able to implement irls on ddR by implementing two distributed functions such as "cross". In truncated SVD only needed to overload two different distributed multiplications. ** Ran these algorithms on the 1000 genome dataset. ** Overall liked ddR since it was easy to implement the algorithms in the package. ** New ideas: *** Trying to separate the data layer from the execution layer *** Create an API that works on "chunks" (which is similar to the "parts" API in ddR). Would like to add these APIs to ddR. *** Indrajit: You should be able to get some of the chunk like features by using parts and dmapply. E.g., you can call dmapply to read 10 different files, which correspond 10 chunks now. These are however wrapped as a darray or dframe. But you can continue to work on the individual chunks by using parts(i). === 6/2/2016 === * Round table introduction * (Michael) Goals for the group: ** Make a common abstraction/interfaces to make it easier to work with distributed data and R ** Unify the interface ** Working group will run for a year. Get an API defined, get at least one open source reference implementations ** not everyone needs to work hands on. We will create smaller groups to focus on those aspects. ** We tried to get a diverse group of participants * Logistics: meet monthly, focus groups may meet more often * R Consoritum may be able to figure ways to fund smaller projects that come out of the working group * Michael Kane: Should we start with an inventory of what is available and people are using? ** Michael Lawrence: Yes, we should find the collection of tools as well as the use cases that are common. ** Joe: I will figure out a wiki space. * Javier: Who are the end users? Simon: Common layer needed to get algorithms working. We started from algos and tried to find the minimal common api. One of the goals is to make sure everyone is on the same page and not trying to create his/her own custom interface. * Javier: Should we try to get people with more algo expertise? * Joe: Simon do you have a stack diagram? * Simon: Can we get R Consortium to help write things up and draw things? * Next meeting: Javier is going to present SparkR next time. a8e8a5c7f86407973bb93fa0ec2417ed7681db46 51 49 2016-11-10T21:14:05Z MichaelLawrence 9 add Hossein Falaki wikitext text/x-wiki == Goals and Purpose == The Distributed Computing Working Group will endorse the design of a common abstraction for distributed data structures in R. We aim to have at least one open-source implementation, as well as a SQL implementation, released within a year of forming the group. == Members == * '''Michael Lawrence''' (Genentech) * '''Indrajit Roy''' (HP Enterprise) * ''Joe Rickert'' (ISC liason, RStudio) * Bernd Bischl (LMU) * Matt Dowle (H2O) * Mario Inchiosa (Microsoft) * Michael Kane (Yale) * Javier Luraschi (RStudio) * Edward Ma (HP Enterprise) * Luke Tierney (University of Iowa) * Simon Urbanek (AT&T) * Bryan Lewis (Paradigm4) * Hossein Falaki (databricks) == Milestones == === Achieved === * Adopt ddR as a prototype for a standard API for distributed computing in R === 2016 Internship === Clark Fitzgerald, a PhD student in the UC Davis Statistics department, worked on ddR and Spark integration. * Wrote [https://github.com/clarkfitzg/sparklite sparklite] and [https://github.com/clarkfitzg/rddlist rddlist] as minimal proof-of-concept R packages to connect and store general data on Spark. [https://docs.google.com/presentation/d/1WfUQ2ockNku90GWMXonEhUEcVOWcgBmWwt5uYSSBYPY/edit?usp=sharing slides] * [https://issues.apache.org/jira/browse/SPARK-16785 Patched SparkR] to allow user defined functions returning binary columns. This allows implementation of different data structures in SparkR. * Updated [https://github.com/vertica/ddR/wiki/Design design documents] with suggested changes to DDR's internal design and object oriented model. * Improved [https://github.com/vertica/ddR/pull/15 testing and ddR internals]. === Outstanding === * Agree on a final standard API for distributed computing in R * Implement at least one scalable backend based on an open-source technology like Spark, SQL, etc == Open Questions == * How can we address the needs of both the end user data scientists and the algorithm implementers? * How should we share data between R and a system like Spark? * Is there any way to unify SparkR and sparklyr? * Could we use the abstractions of tensorflow to partially or fully integrate with platforms like Spark? == Minutes == === 10/13/2016 === ''Detailed minutes were not taken for this meeting'' * Mario Inchiosa: Microsoft's perspective on distributed computing with R ** Microsoft R Server: abstractions and algorithms for distributed computation on top of open-source R ** Desired features of a distributed API like ddR: *** Supports PEMA (initialize, processData, updateResults, processResults) *** Cross-platform *** Fast runtime *** Supports algorithm writer and data scientist *** Comes with a comprehensive set of algorithms *** Easy deployment ** ddR is making good progress but does not yet meet those requirements * Indrajit: ddR progress report and next steps ** Recap of Clark's internship ** Next step: implement some of Clark's design suggestions: https://github.com/vertica/ddR/wiki/Design ** Spark integration will be based on sparklyr ** Should we limit Spark interaction to the DataFrame API or directly interact with RDDs? *** Consensus: will likely need flexibility of RDDs to implement everything we need, e.g., arrays and lists ** Clark and Javier raised concerns about the scalability of sharing data between R and Spark *** Michael: Spark is a platform in its own right, so interoperability is important, should figure something out *** Bryan Lewis: Why not use tensor abstraction from tensorflow? Spark supports tensorflow and an R interface is already in the works. ** Michael raised the issue of additional funding from the R Consortium to continue Clark's work *** Joe Rickert suggested that the working group develop one or more white papers summarizing the findings of the working group for presentation to the Infrastructure Steering Committee. *** Consensus was in favor of this, and several pointed out that the progress so far has been worthwhile, despite not meeting the specific goals laid out in the proposal. * Michael: do we want to invite some external speakers, one per meeting, from groups like databricks, tensorflow, etc? ** Consensus was in favor. === 9/8/2016 === ''Detailed minutes were not taken for this meeting'' * Clark Fitzgerald: internship report ** Developed two packages for low-level Spark integration: rddlist, sparklite ** Patched a bug in Spark ** ddR needs refactoring before Spark integration is feasible: *** dlist, dframe, and darray should be formal classes. *** Partitions of data should be represented by a distributed list abstraction, and most functions (e.g., dmapply) should be implemented on top of that list. * Javier: sparklyr update ** Preparing for CRAN release ** Mario: what happened to sparkapi? *** Javier: sparkapi has been merged into sparklyr in order to avoid overhead of maintaining two packages. ddR can do everything it needs with sparklyr. * Luke Tierney: Update on the low-level vector abstraction, which might support interfaces like ddR and sparklyr. ** Overall approach seems feasible, but still working out a few details. ** Will land in a branch soon. * Bernd Bischl: update on the batchtools package ** Successor to BatchJobs based on in-memory database === 8/11/2016 === ''Meeting was canceled due to lack of availability.'' === 7/14/2016 === * Introduced Clark who is the intern funded by R Consortium. Clark is a graduate student from UC Davis. He will work on ddR integration with Spark and improving the core ddR API as well such as adding a distributed apply() for matrices, split function, etc. * Bernd: Can I play around with ddR now? What backend should I use? How robust is the code? ** Clark: It's in good enough shape to be played around with. We will continue to improve it. Hopefully the spark integration will be done before the end of my internship in September. * Q: Is anyone working on using ddR to make ML scale better. ** Indrajit: We have kmeans, glm, etc. already in CRAN. ** Michael Kane: We are working on glmnet and other packages related to algorithm development. * Javier gave a demo of sparklyr and sparkapi. ** Motivation for the pacakage: The SparkR package overrides the dplyr interface. This is an issue for RStudio. SparkR is not a CRAN package which makes it difficult to add changes. dplyr is the most popular tool by RStudio and is broken on SparkR. ** Sparklyr provides a dplyr interface. It will also support ML like interfaces, such as consuming a ML model. ** Sparklyr does not currently support any distributed computing features. Instead we can recommend ddR as the distributed computing framework on top of sparkapi. We will put the code in CRAN in a couple of weeks. ** Simon: Can you talk more about the wrapper/low level API to work with Spark? *** Javier: The package underneath the cover is called "sparkapi" it is to be used by pacakge builders. "spark_context()" and "invoke()" are the functionality to call scala methods. It does not you to currently run R user defined functions. I am currently working on enabling that feature. Depending upon the interest in using ddR with sparkapi, I can spend more time to make sparkapi feature rich. ** Indrajit: What versions of Spark are supported *** Javier: Anything after 1.6 ** Bernd: How do you export data? *** Javier: We are using all the code from SparkR. So everything in SparkR should continue to work. We don't need to change SparkR. We just need to maintain compatibility. ** Bernd: What happens when the RDDs are very large? *** Javier: Spark will spill on disk. * Michael Kane: Presented examples that he implemented on ddR. ** Talked about how the different distributed packages compare to each other in terms of functionality. ** Michael K. looked at glm and truncated SVD on ddR. Was able to implement irls on ddR by implementing two distributed functions such as "cross". In truncated SVD only needed to overload two different distributed multiplications. ** Ran these algorithms on the 1000 genome dataset. ** Overall liked ddR since it was easy to implement the algorithms in the package. ** New ideas: *** Trying to separate the data layer from the execution layer *** Create an API that works on "chunks" (which is similar to the "parts" API in ddR). Would like to add these APIs to ddR. *** Indrajit: You should be able to get some of the chunk like features by using parts and dmapply. E.g., you can call dmapply to read 10 different files, which correspond 10 chunks now. These are however wrapped as a darray or dframe. But you can continue to work on the individual chunks by using parts(i). === 6/2/2016 === * Round table introduction * (Michael) Goals for the group: ** Make a common abstraction/interfaces to make it easier to work with distributed data and R ** Unify the interface ** Working group will run for a year. Get an API defined, get at least one open source reference implementations ** not everyone needs to work hands on. We will create smaller groups to focus on those aspects. ** We tried to get a diverse group of participants * Logistics: meet monthly, focus groups may meet more often * R Consoritum may be able to figure ways to fund smaller projects that come out of the working group * Michael Kane: Should we start with an inventory of what is available and people are using? ** Michael Lawrence: Yes, we should find the collection of tools as well as the use cases that are common. ** Joe: I will figure out a wiki space. * Javier: Who are the end users? Simon: Common layer needed to get algorithms working. We started from algos and tried to find the minimal common api. One of the goals is to make sure everyone is on the same page and not trying to create his/her own custom interface. * Javier: Should we try to get people with more algo expertise? * Joe: Simon do you have a stack diagram? * Simon: Can we get R Consortium to help write things up and draw things? * Next meeting: Javier is going to present SparkR next time. 79a4a47613792889357ff874f4f8dfd8f538b260 49 48 2016-10-24T23:45:46Z Clarkfitzg 13 /* 2016 Internship */ wikitext text/x-wiki == Goals and Purpose == The Distributed Computing Working Group will endorse the design of a common abstraction for distributed data structures in R. We aim to have at least one open-source implementation, as well as a SQL implementation, released within a year of forming the group. == Members == * '''Michael Lawrence''' (Genentech) * '''Indrajit Roy''' (HP Enterprise) * ''Joe Rickert'' (ISC liason, RStudio) * Bernd Bischl (LMU) * Matt Dowle (H2O) * Mario Inchiosa (Microsoft) * Michael Kane (Yale) * Javier Luraschi (RStudio) * Edward Ma (HP Enterprise) * Luke Tierney (University of Iowa) * Simon Urbanek (AT&T) * Bryan Lewis (Paradigm4) == Milestones == === Achieved === * Adopt ddR as a prototype for a standard API for distributed computing in R === 2016 Internship === Clark Fitzgerald, a PhD student in the UC Davis Statistics department, worked on ddR and Spark integration. * Wrote [https://github.com/clarkfitzg/sparklite sparklite] and [https://github.com/clarkfitzg/rddlist rddlist] as minimal proof-of-concept R packages to connect and store general data on Spark. [https://docs.google.com/presentation/d/1WfUQ2ockNku90GWMXonEhUEcVOWcgBmWwt5uYSSBYPY/edit?usp=sharing slides] * [https://issues.apache.org/jira/browse/SPARK-16785 Patched SparkR] to allow user defined functions returning binary columns. This allows implementation of different data structures in SparkR. * Updated [https://github.com/vertica/ddR/wiki/Design design documents] with suggested changes to DDR's internal design and object oriented model. * Improved [https://github.com/vertica/ddR/pull/15 testing and ddR internals]. === Outstanding === * Agree on a final standard API for distributed computing in R * Implement at least one scalable backend based on an open-source technology like Spark, SQL, etc == Open Questions == * How can we address the needs of both the end user data scientists and the algorithm implementers? * How should we share data between R and a system like Spark? * Is there any way to unify SparkR and sparklyr? * Could we use the abstractions of tensorflow to partially or fully integrate with platforms like Spark? == Minutes == === 10/13/2016 === ''Detailed minutes were not taken for this meeting'' * Mario Inchiosa: Microsoft's perspective on distributed computing with R ** Microsoft R Server: abstractions and algorithms for distributed computation on top of open-source R ** Desired features of a distributed API like ddR: *** Supports PEMA (initialize, processData, updateResults, processResults) *** Cross-platform *** Fast runtime *** Supports algorithm writer and data scientist *** Comes with a comprehensive set of algorithms *** Easy deployment ** ddR is making good progress but does not yet meet those requirements * Indrajit: ddR progress report and next steps ** Recap of Clark's internship ** Next step: implement some of Clark's design suggestions: https://github.com/vertica/ddR/wiki/Design ** Spark integration will be based on sparklyr ** Should we limit Spark interaction to the DataFrame API or directly interact with RDDs? *** Consensus: will likely need flexibility of RDDs to implement everything we need, e.g., arrays and lists ** Clark and Javier raised concerns about the scalability of sharing data between R and Spark *** Michael: Spark is a platform in its own right, so interoperability is important, should figure something out *** Bryan Lewis: Why not use tensor abstraction from tensorflow? Spark supports tensorflow and an R interface is already in the works. ** Michael raised the issue of additional funding from the R Consortium to continue Clark's work *** Joe Rickert suggested that the working group develop one or more white papers summarizing the findings of the working group for presentation to the Infrastructure Steering Committee. *** Consensus was in favor of this, and several pointed out that the progress so far has been worthwhile, despite not meeting the specific goals laid out in the proposal. * Michael: do we want to invite some external speakers, one per meeting, from groups like databricks, tensorflow, etc? ** Consensus was in favor. === 9/8/2016 === ''Detailed minutes were not taken for this meeting'' * Clark Fitzgerald: internship report ** Developed two packages for low-level Spark integration: rddlist, sparklite ** Patched a bug in Spark ** ddR needs refactoring before Spark integration is feasible: *** dlist, dframe, and darray should be formal classes. *** Partitions of data should be represented by a distributed list abstraction, and most functions (e.g., dmapply) should be implemented on top of that list. * Javier: sparklyr update ** Preparing for CRAN release ** Mario: what happened to sparkapi? *** Javier: sparkapi has been merged into sparklyr in order to avoid overhead of maintaining two packages. ddR can do everything it needs with sparklyr. * Luke Tierney: Update on the low-level vector abstraction, which might support interfaces like ddR and sparklyr. ** Overall approach seems feasible, but still working out a few details. ** Will land in a branch soon. * Bernd Bischl: update on the batchtools package ** Successor to BatchJobs based on in-memory database === 8/11/2016 === ''Meeting was canceled due to lack of availability.'' === 7/14/2016 === * Introduced Clark who is the intern funded by R Consortium. Clark is a graduate student from UC Davis. He will work on ddR integration with Spark and improving the core ddR API as well such as adding a distributed apply() for matrices, split function, etc. * Bernd: Can I play around with ddR now? What backend should I use? How robust is the code? ** Clark: It's in good enough shape to be played around with. We will continue to improve it. Hopefully the spark integration will be done before the end of my internship in September. * Q: Is anyone working on using ddR to make ML scale better. ** Indrajit: We have kmeans, glm, etc. already in CRAN. ** Michael Kane: We are working on glmnet and other packages related to algorithm development. * Javier gave a demo of sparklyr and sparkapi. ** Motivation for the pacakage: The SparkR package overrides the dplyr interface. This is an issue for RStudio. SparkR is not a CRAN package which makes it difficult to add changes. dplyr is the most popular tool by RStudio and is broken on SparkR. ** Sparklyr provides a dplyr interface. It will also support ML like interfaces, such as consuming a ML model. ** Sparklyr does not currently support any distributed computing features. Instead we can recommend ddR as the distributed computing framework on top of sparkapi. We will put the code in CRAN in a couple of weeks. ** Simon: Can you talk more about the wrapper/low level API to work with Spark? *** Javier: The package underneath the cover is called "sparkapi" it is to be used by pacakge builders. "spark_context()" and "invoke()" are the functionality to call scala methods. It does not you to currently run R user defined functions. I am currently working on enabling that feature. Depending upon the interest in using ddR with sparkapi, I can spend more time to make sparkapi feature rich. ** Indrajit: What versions of Spark are supported *** Javier: Anything after 1.6 ** Bernd: How do you export data? *** Javier: We are using all the code from SparkR. So everything in SparkR should continue to work. We don't need to change SparkR. We just need to maintain compatibility. ** Bernd: What happens when the RDDs are very large? *** Javier: Spark will spill on disk. * Michael Kane: Presented examples that he implemented on ddR. ** Talked about how the different distributed packages compare to each other in terms of functionality. ** Michael K. looked at glm and truncated SVD on ddR. Was able to implement irls on ddR by implementing two distributed functions such as "cross". In truncated SVD only needed to overload two different distributed multiplications. ** Ran these algorithms on the 1000 genome dataset. ** Overall liked ddR since it was easy to implement the algorithms in the package. ** New ideas: *** Trying to separate the data layer from the execution layer *** Create an API that works on "chunks" (which is similar to the "parts" API in ddR). Would like to add these APIs to ddR. *** Indrajit: You should be able to get some of the chunk like features by using parts and dmapply. E.g., you can call dmapply to read 10 different files, which correspond 10 chunks now. These are however wrapped as a darray or dframe. But you can continue to work on the individual chunks by using parts(i). === 6/2/2016 === * Round table introduction * (Michael) Goals for the group: ** Make a common abstraction/interfaces to make it easier to work with distributed data and R ** Unify the interface ** Working group will run for a year. Get an API defined, get at least one open source reference implementations ** not everyone needs to work hands on. We will create smaller groups to focus on those aspects. ** We tried to get a diverse group of participants * Logistics: meet monthly, focus groups may meet more often * R Consoritum may be able to figure ways to fund smaller projects that come out of the working group * Michael Kane: Should we start with an inventory of what is available and people are using? ** Michael Lawrence: Yes, we should find the collection of tools as well as the use cases that are common. ** Joe: I will figure out a wiki space. * Javier: Who are the end users? Simon: Common layer needed to get algorithms working. We started from algos and tried to find the minimal common api. One of the goals is to make sure everyone is on the same page and not trying to create his/her own custom interface. * Javier: Should we try to get people with more algo expertise? * Joe: Simon do you have a stack diagram? * Simon: Can we get R Consortium to help write things up and draw things? * Next meeting: Javier is going to present SparkR next time. 238f3d2a58158ca605d909fa25dceff075e9f9a0 48 46 2016-10-24T23:36:59Z Clarkfitzg 13 /* Milestones */ wikitext text/x-wiki == Goals and Purpose == The Distributed Computing Working Group will endorse the design of a common abstraction for distributed data structures in R. We aim to have at least one open-source implementation, as well as a SQL implementation, released within a year of forming the group. == Members == * '''Michael Lawrence''' (Genentech) * '''Indrajit Roy''' (HP Enterprise) * ''Joe Rickert'' (ISC liason, RStudio) * Bernd Bischl (LMU) * Matt Dowle (H2O) * Mario Inchiosa (Microsoft) * Michael Kane (Yale) * Javier Luraschi (RStudio) * Edward Ma (HP Enterprise) * Luke Tierney (University of Iowa) * Simon Urbanek (AT&T) * Bryan Lewis (Paradigm4) == Milestones == === Achieved === * Adopt ddR as a prototype for a standard API for distributed computing in R === 2016 Internship === Clark Fitzgerald, a PhD student in the UC Davis Statistics department, worked on ddR and Spark integration. * Wrote [https://github.com/clarkfitzg/sparklite sparklite] and * [https://issues.apache.org/jira/browse/SPARK-16785 Patched SparkR] to allow user defined functions returning binary columns. This allows implementation of different data structures in SparkR. * Updated [https://github.com/vertica/ddR/wiki/Design design documents] with suggested changes to DDR's internal design and object oriented model. * Began === Outstanding === * Agree on a final standard API for distributed computing in R * Implement at least one scalable backend based on an open-source technology like Spark, SQL, etc == Open Questions == * How can we address the needs of both the end user data scientists and the algorithm implementers? * How should we share data between R and a system like Spark? * Is there any way to unify SparkR and sparklyr? * Could we use the abstractions of tensorflow to partially or fully integrate with platforms like Spark? == Minutes == === 10/13/2016 === ''Detailed minutes were not taken for this meeting'' * Mario Inchiosa: Microsoft's perspective on distributed computing with R ** Microsoft R Server: abstractions and algorithms for distributed computation on top of open-source R ** Desired features of a distributed API like ddR: *** Supports PEMA (initialize, processData, updateResults, processResults) *** Cross-platform *** Fast runtime *** Supports algorithm writer and data scientist *** Comes with a comprehensive set of algorithms *** Easy deployment ** ddR is making good progress but does not yet meet those requirements * Indrajit: ddR progress report and next steps ** Recap of Clark's internship ** Next step: implement some of Clark's design suggestions: https://github.com/vertica/ddR/wiki/Design ** Spark integration will be based on sparklyr ** Should we limit Spark interaction to the DataFrame API or directly interact with RDDs? *** Consensus: will likely need flexibility of RDDs to implement everything we need, e.g., arrays and lists ** Clark and Javier raised concerns about the scalability of sharing data between R and Spark *** Michael: Spark is a platform in its own right, so interoperability is important, should figure something out *** Bryan Lewis: Why not use tensor abstraction from tensorflow? Spark supports tensorflow and an R interface is already in the works. ** Michael raised the issue of additional funding from the R Consortium to continue Clark's work *** Joe Rickert suggested that the working group develop one or more white papers summarizing the findings of the working group for presentation to the Infrastructure Steering Committee. *** Consensus was in favor of this, and several pointed out that the progress so far has been worthwhile, despite not meeting the specific goals laid out in the proposal. * Michael: do we want to invite some external speakers, one per meeting, from groups like databricks, tensorflow, etc? ** Consensus was in favor. === 9/8/2016 === ''Detailed minutes were not taken for this meeting'' * Clark Fitzgerald: internship report ** Developed two packages for low-level Spark integration: rddlist, sparklite ** Patched a bug in Spark ** ddR needs refactoring before Spark integration is feasible: *** dlist, dframe, and darray should be formal classes. *** Partitions of data should be represented by a distributed list abstraction, and most functions (e.g., dmapply) should be implemented on top of that list. * Javier: sparklyr update ** Preparing for CRAN release ** Mario: what happened to sparkapi? *** Javier: sparkapi has been merged into sparklyr in order to avoid overhead of maintaining two packages. ddR can do everything it needs with sparklyr. * Luke Tierney: Update on the low-level vector abstraction, which might support interfaces like ddR and sparklyr. ** Overall approach seems feasible, but still working out a few details. ** Will land in a branch soon. * Bernd Bischl: update on the batchtools package ** Successor to BatchJobs based on in-memory database === 8/11/2016 === ''Meeting was canceled due to lack of availability.'' === 7/14/2016 === * Introduced Clark who is the intern funded by R Consortium. Clark is a graduate student from UC Davis. He will work on ddR integration with Spark and improving the core ddR API as well such as adding a distributed apply() for matrices, split function, etc. * Bernd: Can I play around with ddR now? What backend should I use? How robust is the code? ** Clark: It's in good enough shape to be played around with. We will continue to improve it. Hopefully the spark integration will be done before the end of my internship in September. * Q: Is anyone working on using ddR to make ML scale better. ** Indrajit: We have kmeans, glm, etc. already in CRAN. ** Michael Kane: We are working on glmnet and other packages related to algorithm development. * Javier gave a demo of sparklyr and sparkapi. ** Motivation for the pacakage: The SparkR package overrides the dplyr interface. This is an issue for RStudio. SparkR is not a CRAN package which makes it difficult to add changes. dplyr is the most popular tool by RStudio and is broken on SparkR. ** Sparklyr provides a dplyr interface. It will also support ML like interfaces, such as consuming a ML model. ** Sparklyr does not currently support any distributed computing features. Instead we can recommend ddR as the distributed computing framework on top of sparkapi. We will put the code in CRAN in a couple of weeks. ** Simon: Can you talk more about the wrapper/low level API to work with Spark? *** Javier: The package underneath the cover is called "sparkapi" it is to be used by pacakge builders. "spark_context()" and "invoke()" are the functionality to call scala methods. It does not you to currently run R user defined functions. I am currently working on enabling that feature. Depending upon the interest in using ddR with sparkapi, I can spend more time to make sparkapi feature rich. ** Indrajit: What versions of Spark are supported *** Javier: Anything after 1.6 ** Bernd: How do you export data? *** Javier: We are using all the code from SparkR. So everything in SparkR should continue to work. We don't need to change SparkR. We just need to maintain compatibility. ** Bernd: What happens when the RDDs are very large? *** Javier: Spark will spill on disk. * Michael Kane: Presented examples that he implemented on ddR. ** Talked about how the different distributed packages compare to each other in terms of functionality. ** Michael K. looked at glm and truncated SVD on ddR. Was able to implement irls on ddR by implementing two distributed functions such as "cross". In truncated SVD only needed to overload two different distributed multiplications. ** Ran these algorithms on the 1000 genome dataset. ** Overall liked ddR since it was easy to implement the algorithms in the package. ** New ideas: *** Trying to separate the data layer from the execution layer *** Create an API that works on "chunks" (which is similar to the "parts" API in ddR). Would like to add these APIs to ddR. *** Indrajit: You should be able to get some of the chunk like features by using parts and dmapply. E.g., you can call dmapply to read 10 different files, which correspond 10 chunks now. These are however wrapped as a darray or dframe. But you can continue to work on the individual chunks by using parts(i). === 6/2/2016 === * Round table introduction * (Michael) Goals for the group: ** Make a common abstraction/interfaces to make it easier to work with distributed data and R ** Unify the interface ** Working group will run for a year. Get an API defined, get at least one open source reference implementations ** not everyone needs to work hands on. We will create smaller groups to focus on those aspects. ** We tried to get a diverse group of participants * Logistics: meet monthly, focus groups may meet more often * R Consoritum may be able to figure ways to fund smaller projects that come out of the working group * Michael Kane: Should we start with an inventory of what is available and people are using? ** Michael Lawrence: Yes, we should find the collection of tools as well as the use cases that are common. ** Joe: I will figure out a wiki space. * Javier: Who are the end users? Simon: Common layer needed to get algorithms working. We started from algos and tried to find the minimal common api. One of the goals is to make sure everyone is on the same page and not trying to create his/her own custom interface. * Javier: Should we try to get people with more algo expertise? * Joe: Simon do you have a stack diagram? * Simon: Can we get R Consortium to help write things up and draw things? * Next meeting: Javier is going to present SparkR next time. 5cee7c740dac6df93c25d1ac5d908bd54f2eed12 46 45 2016-10-18T16:52:07Z MichaelLawrence 9 change Joe's affiliation wikitext text/x-wiki == Goals and Purpose == The Distributed Computing Working Group will endorse the design of a common abstraction for distributed data structures in R. We aim to have at least one open-source implementation, as well as a SQL implementation, released within a year of forming the group. == Members == * '''Michael Lawrence''' (Genentech) * '''Indrajit Roy''' (HP Enterprise) * ''Joe Rickert'' (ISC liason, RStudio) * Bernd Bischl (LMU) * Matt Dowle (H2O) * Mario Inchiosa (Microsoft) * Michael Kane (Yale) * Javier Luraschi (RStudio) * Edward Ma (HP Enterprise) * Luke Tierney (University of Iowa) * Simon Urbanek (AT&T) * Bryan Lewis (Paradigm4) == Milestones == === Achieved === * Adopt ddR as a prototype for a standard API for distributed computing in R * Explore the interfacing of R and Spark in the context of the ddR package (Clark Fitzgerald) === Outstanding === * Agree on a final standard API for distributed computing in R * Implement at least one scalable backend based on an open-source technology like Spark, SQL, etc == Open Questions == * How can we address the needs of both the end user data scientists and the algorithm implementers? * How should we share data between R and a system like Spark? * Is there any way to unify SparkR and sparklyr? * Could we use the abstractions of tensorflow to partially or fully integrate with platforms like Spark? == Minutes == === 10/13/2016 === ''Detailed minutes were not taken for this meeting'' * Mario Inchiosa: Microsoft's perspective on distributed computing with R ** Microsoft R Server: abstractions and algorithms for distributed computation on top of open-source R ** Desired features of a distributed API like ddR: *** Supports PEMA (initialize, processData, updateResults, processResults) *** Cross-platform *** Fast runtime *** Supports algorithm writer and data scientist *** Comes with a comprehensive set of algorithms *** Easy deployment ** ddR is making good progress but does not yet meet those requirements * Indrajit: ddR progress report and next steps ** Recap of Clark's internship ** Next step: implement some of Clark's design suggestions: https://github.com/vertica/ddR/wiki/Design ** Spark integration will be based on sparklyr ** Should we limit Spark interaction to the DataFrame API or directly interact with RDDs? *** Consensus: will likely need flexibility of RDDs to implement everything we need, e.g., arrays and lists ** Clark and Javier raised concerns about the scalability of sharing data between R and Spark *** Michael: Spark is a platform in its own right, so interoperability is important, should figure something out *** Bryan Lewis: Why not use tensor abstraction from tensorflow? Spark supports tensorflow and an R interface is already in the works. ** Michael raised the issue of additional funding from the R Consortium to continue Clark's work *** Joe Rickert suggested that the working group develop one or more white papers summarizing the findings of the working group for presentation to the Infrastructure Steering Committee. *** Consensus was in favor of this, and several pointed out that the progress so far has been worthwhile, despite not meeting the specific goals laid out in the proposal. * Michael: do we want to invite some external speakers, one per meeting, from groups like databricks, tensorflow, etc? ** Consensus was in favor. === 9/8/2016 === ''Detailed minutes were not taken for this meeting'' * Clark Fitzgerald: internship report ** Developed two packages for low-level Spark integration: rddlist, sparklite ** Patched a bug in Spark ** ddR needs refactoring before Spark integration is feasible: *** dlist, dframe, and darray should be formal classes. *** Partitions of data should be represented by a distributed list abstraction, and most functions (e.g., dmapply) should be implemented on top of that list. * Javier: sparklyr update ** Preparing for CRAN release ** Mario: what happened to sparkapi? *** Javier: sparkapi has been merged into sparklyr in order to avoid overhead of maintaining two packages. ddR can do everything it needs with sparklyr. * Luke Tierney: Update on the low-level vector abstraction, which might support interfaces like ddR and sparklyr. ** Overall approach seems feasible, but still working out a few details. ** Will land in a branch soon. * Bernd Bischl: update on the batchtools package ** Successor to BatchJobs based on in-memory database === 8/11/2016 === ''Meeting was canceled due to lack of availability.'' === 7/14/2016 === * Introduced Clark who is the intern funded by R Consortium. Clark is a graduate student from UC Davis. He will work on ddR integration with Spark and improving the core ddR API as well such as adding a distributed apply() for matrices, split function, etc. * Bernd: Can I play around with ddR now? What backend should I use? How robust is the code? ** Clark: It's in good enough shape to be played around with. We will continue to improve it. Hopefully the spark integration will be done before the end of my internship in September. * Q: Is anyone working on using ddR to make ML scale better. ** Indrajit: We have kmeans, glm, etc. already in CRAN. ** Michael Kane: We are working on glmnet and other packages related to algorithm development. * Javier gave a demo of sparklyr and sparkapi. ** Motivation for the pacakage: The SparkR package overrides the dplyr interface. This is an issue for RStudio. SparkR is not a CRAN package which makes it difficult to add changes. dplyr is the most popular tool by RStudio and is broken on SparkR. ** Sparklyr provides a dplyr interface. It will also support ML like interfaces, such as consuming a ML model. ** Sparklyr does not currently support any distributed computing features. Instead we can recommend ddR as the distributed computing framework on top of sparkapi. We will put the code in CRAN in a couple of weeks. ** Simon: Can you talk more about the wrapper/low level API to work with Spark? *** Javier: The package underneath the cover is called "sparkapi" it is to be used by pacakge builders. "spark_context()" and "invoke()" are the functionality to call scala methods. It does not you to currently run R user defined functions. I am currently working on enabling that feature. Depending upon the interest in using ddR with sparkapi, I can spend more time to make sparkapi feature rich. ** Indrajit: What versions of Spark are supported *** Javier: Anything after 1.6 ** Bernd: How do you export data? *** Javier: We are using all the code from SparkR. So everything in SparkR should continue to work. We don't need to change SparkR. We just need to maintain compatibility. ** Bernd: What happens when the RDDs are very large? *** Javier: Spark will spill on disk. * Michael Kane: Presented examples that he implemented on ddR. ** Talked about how the different distributed packages compare to each other in terms of functionality. ** Michael K. looked at glm and truncated SVD on ddR. Was able to implement irls on ddR by implementing two distributed functions such as "cross". In truncated SVD only needed to overload two different distributed multiplications. ** Ran these algorithms on the 1000 genome dataset. ** Overall liked ddR since it was easy to implement the algorithms in the package. ** New ideas: *** Trying to separate the data layer from the execution layer *** Create an API that works on "chunks" (which is similar to the "parts" API in ddR). Would like to add these APIs to ddR. *** Indrajit: You should be able to get some of the chunk like features by using parts and dmapply. E.g., you can call dmapply to read 10 different files, which correspond 10 chunks now. These are however wrapped as a darray or dframe. But you can continue to work on the individual chunks by using parts(i). === 6/2/2016 === * Round table introduction * (Michael) Goals for the group: ** Make a common abstraction/interfaces to make it easier to work with distributed data and R ** Unify the interface ** Working group will run for a year. Get an API defined, get at least one open source reference implementations ** not everyone needs to work hands on. We will create smaller groups to focus on those aspects. ** We tried to get a diverse group of participants * Logistics: meet monthly, focus groups may meet more often * R Consoritum may be able to figure ways to fund smaller projects that come out of the working group * Michael Kane: Should we start with an inventory of what is available and people are using? ** Michael Lawrence: Yes, we should find the collection of tools as well as the use cases that are common. ** Joe: I will figure out a wiki space. * Javier: Who are the end users? Simon: Common layer needed to get algorithms working. We started from algos and tried to find the minimal common api. One of the goals is to make sure everyone is on the same page and not trying to create his/her own custom interface. * Javier: Should we try to get people with more algo expertise? * Joe: Simon do you have a stack diagram? * Simon: Can we get R Consortium to help write things up and draw things? * Next meeting: Javier is going to present SparkR next time. fa35c57132996d4d6d161d54fb2414d7e51a56a1 45 44 2016-10-18T16:27:16Z MichaelLawrence 9 wikitext text/x-wiki == Goals and Purpose == The Distributed Computing Working Group will endorse the design of a common abstraction for distributed data structures in R. We aim to have at least one open-source implementation, as well as a SQL implementation, released within a year of forming the group. == Members == * '''Michael Lawrence''' (Genentech) * '''Indrajit Roy''' (HP Enterprise) * ''Joe Rickert'' (Microsoft) * Bernd Bischl (LMU) * Matt Dowle (H2O) * Mario Inchiosa (Microsoft) * Michael Kane (Yale) * Javier Luraschi (RStudio) * Edward Ma (HP Enterprise) * Luke Tierney (University of Iowa) * Simon Urbanek (AT&T) * Bryan Lewis (Paradigm4) == Milestones == === Achieved === * Adopt ddR as a prototype for a standard API for distributed computing in R * Explore the interfacing of R and Spark in the context of the ddR package (Clark Fitzgerald) === Outstanding === * Agree on a final standard API for distributed computing in R * Implement at least one scalable backend based on an open-source technology like Spark, SQL, etc == Open Questions == * How can we address the needs of both the end user data scientists and the algorithm implementers? * How should we share data between R and a system like Spark? * Is there any way to unify SparkR and sparklyr? * Could we use the abstractions of tensorflow to partially or fully integrate with platforms like Spark? == Minutes == === 10/13/2016 === ''Detailed minutes were not taken for this meeting'' * Mario Inchiosa: Microsoft's perspective on distributed computing with R ** Microsoft R Server: abstractions and algorithms for distributed computation on top of open-source R ** Desired features of a distributed API like ddR: *** Supports PEMA (initialize, processData, updateResults, processResults) *** Cross-platform *** Fast runtime *** Supports algorithm writer and data scientist *** Comes with a comprehensive set of algorithms *** Easy deployment ** ddR is making good progress but does not yet meet those requirements * Indrajit: ddR progress report and next steps ** Recap of Clark's internship ** Next step: implement some of Clark's design suggestions: https://github.com/vertica/ddR/wiki/Design ** Spark integration will be based on sparklyr ** Should we limit Spark interaction to the DataFrame API or directly interact with RDDs? *** Consensus: will likely need flexibility of RDDs to implement everything we need, e.g., arrays and lists ** Clark and Javier raised concerns about the scalability of sharing data between R and Spark *** Michael: Spark is a platform in its own right, so interoperability is important, should figure something out *** Bryan Lewis: Why not use tensor abstraction from tensorflow? Spark supports tensorflow and an R interface is already in the works. ** Michael raised the issue of additional funding from the R Consortium to continue Clark's work *** Joe Rickert suggested that the working group develop one or more white papers summarizing the findings of the working group for presentation to the Infrastructure Steering Committee. *** Consensus was in favor of this, and several pointed out that the progress so far has been worthwhile, despite not meeting the specific goals laid out in the proposal. * Michael: do we want to invite some external speakers, one per meeting, from groups like databricks, tensorflow, etc? ** Consensus was in favor. === 9/8/2016 === ''Detailed minutes were not taken for this meeting'' * Clark Fitzgerald: internship report ** Developed two packages for low-level Spark integration: rddlist, sparklite ** Patched a bug in Spark ** ddR needs refactoring before Spark integration is feasible: *** dlist, dframe, and darray should be formal classes. *** Partitions of data should be represented by a distributed list abstraction, and most functions (e.g., dmapply) should be implemented on top of that list. * Javier: sparklyr update ** Preparing for CRAN release ** Mario: what happened to sparkapi? *** Javier: sparkapi has been merged into sparklyr in order to avoid overhead of maintaining two packages. ddR can do everything it needs with sparklyr. * Luke Tierney: Update on the low-level vector abstraction, which might support interfaces like ddR and sparklyr. ** Overall approach seems feasible, but still working out a few details. ** Will land in a branch soon. * Bernd Bischl: update on the batchtools package ** Successor to BatchJobs based on in-memory database === 8/11/2016 === ''Meeting was canceled due to lack of availability.'' === 7/14/2016 === * Introduced Clark who is the intern funded by R Consortium. Clark is a graduate student from UC Davis. He will work on ddR integration with Spark and improving the core ddR API as well such as adding a distributed apply() for matrices, split function, etc. * Bernd: Can I play around with ddR now? What backend should I use? How robust is the code? ** Clark: It's in good enough shape to be played around with. We will continue to improve it. Hopefully the spark integration will be done before the end of my internship in September. * Q: Is anyone working on using ddR to make ML scale better. ** Indrajit: We have kmeans, glm, etc. already in CRAN. ** Michael Kane: We are working on glmnet and other packages related to algorithm development. * Javier gave a demo of sparklyr and sparkapi. ** Motivation for the pacakage: The SparkR package overrides the dplyr interface. This is an issue for RStudio. SparkR is not a CRAN package which makes it difficult to add changes. dplyr is the most popular tool by RStudio and is broken on SparkR. ** Sparklyr provides a dplyr interface. It will also support ML like interfaces, such as consuming a ML model. ** Sparklyr does not currently support any distributed computing features. Instead we can recommend ddR as the distributed computing framework on top of sparkapi. We will put the code in CRAN in a couple of weeks. ** Simon: Can you talk more about the wrapper/low level API to work with Spark? *** Javier: The package underneath the cover is called "sparkapi" it is to be used by pacakge builders. "spark_context()" and "invoke()" are the functionality to call scala methods. It does not you to currently run R user defined functions. I am currently working on enabling that feature. Depending upon the interest in using ddR with sparkapi, I can spend more time to make sparkapi feature rich. ** Indrajit: What versions of Spark are supported *** Javier: Anything after 1.6 ** Bernd: How do you export data? *** Javier: We are using all the code from SparkR. So everything in SparkR should continue to work. We don't need to change SparkR. We just need to maintain compatibility. ** Bernd: What happens when the RDDs are very large? *** Javier: Spark will spill on disk. * Michael Kane: Presented examples that he implemented on ddR. ** Talked about how the different distributed packages compare to each other in terms of functionality. ** Michael K. looked at glm and truncated SVD on ddR. Was able to implement irls on ddR by implementing two distributed functions such as "cross". In truncated SVD only needed to overload two different distributed multiplications. ** Ran these algorithms on the 1000 genome dataset. ** Overall liked ddR since it was easy to implement the algorithms in the package. ** New ideas: *** Trying to separate the data layer from the execution layer *** Create an API that works on "chunks" (which is similar to the "parts" API in ddR). Would like to add these APIs to ddR. *** Indrajit: You should be able to get some of the chunk like features by using parts and dmapply. E.g., you can call dmapply to read 10 different files, which correspond 10 chunks now. These are however wrapped as a darray or dframe. But you can continue to work on the individual chunks by using parts(i). === 6/2/2016 === * Round table introduction * (Michael) Goals for the group: ** Make a common abstraction/interfaces to make it easier to work with distributed data and R ** Unify the interface ** Working group will run for a year. Get an API defined, get at least one open source reference implementations ** not everyone needs to work hands on. We will create smaller groups to focus on those aspects. ** We tried to get a diverse group of participants * Logistics: meet monthly, focus groups may meet more often * R Consoritum may be able to figure ways to fund smaller projects that come out of the working group * Michael Kane: Should we start with an inventory of what is available and people are using? ** Michael Lawrence: Yes, we should find the collection of tools as well as the use cases that are common. ** Joe: I will figure out a wiki space. * Javier: Who are the end users? Simon: Common layer needed to get algorithms working. We started from algos and tried to find the minimal common api. One of the goals is to make sure everyone is on the same page and not trying to create his/her own custom interface. * Javier: Should we try to get people with more algo expertise? * Joe: Simon do you have a stack diagram? * Simon: Can we get R Consortium to help write things up and draw things? * Next meeting: Javier is going to present SparkR next time. f74c61cd75d285a65ff8883c1e0dfc0cf12ecb4e 44 43 2016-10-18T11:52:48Z MichaelLawrence 9 October minutes wikitext text/x-wiki == Goals and Purpose == The Distributed Computing Working Group will endorse the design of a common abstraction for distributed data structures in R. We aim to have at least one open-source implementation, as well as a SQL implementation, released within a year of forming the group. == Members == * '''Michael Lawrence''' (Genentech) * '''Indrajit Roy''' (HP Enterprise) * ''Joe Rickert'' (Microsoft) * Bernd Bischl (LMU) * Matt Dowle (H2O) * Mario Inchiosa (Microsoft) * Michael Kane (Yale) * Javier Luraschi (RStudio) * Edward Ma (HP Enterprise) * Luke Tierney (University of Iowa) * Simon Urbanek (AT&T) == Minutes == === 10/13/2016 === ''Detailed minutes were not taken for this meeting'' * Mario Inchiosa: Microsoft's perspective on distributed computing with R ** Microsoft R Server: abstractions and algorithms for distributed computation on top of open-source R ** Desired features of a distributed API like ddR: *** Supports PEMA (initialize, processData, updateResults, processResults) *** Cross-platform *** Fast runtime *** Supports algorithm writer and data scientist *** Comes with a comprehensive set of algorithms *** Easy deployment ** ddR is making good progress but does not yet meet those requirements * Indrajit: ddR progress report and next steps ** Recap of Clark's internship ** Next step: implement some of Clark's design suggestions: https://github.com/vertica/ddR/wiki/Design ** Spark integration will be based on sparklyr ** Should we limit Spark interaction to the DataFrame API or directly interact with RDDs? *** Consensus: will likely need flexibility of RDDs to implement everything we need, e.g., arrays and lists ** Clark and Javier raised concerns about the scalability of sharing data between R and Spark *** Michael: Spark is a platform in its own right, so interoperability is important, should figure something out *** Bryan Lewis: Why not use tensor abstraction from tensorflow? Spark supports tensorflow and an R interface is already in the works. ** Michael raised the issue of additional funding from the R Consortium to continue Clark's work *** Joe Rickert suggested that the working group develop one or more white papers summarizing the findings of the working group for presentation to the Infrastructure Steering Committee. *** Consensus was in favor of this, and several pointed out that the progress so far has been worthwhile, despite not meeting the specific goals laid out in the proposal. * Michael: do we want to invite some external speakers, one per meeting, from groups like databricks, tensorflow, etc? ** Consensus was in favor. === 9/8/2016 === ''Detailed minutes were not taken for this meeting'' * Clark Fitzgerald: internship report ** Developed two packages for low-level Spark integration: rddlist, sparklite ** Patched a bug in Spark ** ddR needs refactoring before Spark integration is feasible: *** dlist, dframe, and darray should be formal classes. *** Partitions of data should be represented by a distributed list abstraction, and most functions (e.g., dmapply) should be implemented on top of that list. * Javier: sparklyr update ** Preparing for CRAN release ** Mario: what happened to sparkapi? *** Javier: sparkapi has been merged into sparklyr in order to avoid overhead of maintaining two packages. ddR can do everything it needs with sparklyr. * Luke Tierney: Update on the low-level vector abstraction, which might support interfaces like ddR and sparklyr. ** Overall approach seems feasible, but still working out a few details. ** Will land in a branch soon. * Bernd Bischl: update on the batchtools package ** Successor to BatchJobs based on in-memory database === 8/11/2016 === ''Meeting was canceled due to lack of availability.'' === 7/14/2016 === * Introduced Clark who is the intern funded by R Consortium. Clark is a graduate student from UC Davis. He will work on ddR integration with Spark and improving the core ddR API as well such as adding a distributed apply() for matrices, split function, etc. * Bernd: Can I play around with ddR now? What backend should I use? How robust is the code? ** Clark: It's in good enough shape to be played around with. We will continue to improve it. Hopefully the spark integration will be done before the end of my internship in September. * Q: Is anyone working on using ddR to make ML scale better. ** Indrajit: We have kmeans, glm, etc. already in CRAN. ** Michael Kane: We are working on glmnet and other packages related to algorithm development. * Javier gave a demo of sparklyr and sparkapi. ** Motivation for the pacakage: The SparkR package overrides the dplyr interface. This is an issue for RStudio. SparkR is not a CRAN package which makes it difficult to add changes. dplyr is the most popular tool by RStudio and is broken on SparkR. ** Sparklyr provides a dplyr interface. It will also support ML like interfaces, such as consuming a ML model. ** Sparklyr does not currently support any distributed computing features. Instead we can recommend ddR as the distributed computing framework on top of sparkapi. We will put the code in CRAN in a couple of weeks. ** Simon: Can you talk more about the wrapper/low level API to work with Spark? *** Javier: The package underneath the cover is called "sparkapi" it is to be used by pacakge builders. "spark_context()" and "invoke()" are the functionality to call scala methods. It does not you to currently run R user defined functions. I am currently working on enabling that feature. Depending upon the interest in using ddR with sparkapi, I can spend more time to make sparkapi feature rich. ** Indrajit: What versions of Spark are supported *** Javier: Anything after 1.6 ** Bernd: How do you export data? *** Javier: We are using all the code from SparkR. So everything in SparkR should continue to work. We don't need to change SparkR. We just need to maintain compatibility. ** Bernd: What happens when the RDDs are very large? *** Javier: Spark will spill on disk. * Michael Kane: Presented examples that he implemented on ddR. ** Talked about how the different distributed packages compare to each other in terms of functionality. ** Michael K. looked at glm and truncated SVD on ddR. Was able to implement irls on ddR by implementing two distributed functions such as "cross". In truncated SVD only needed to overload two different distributed multiplications. ** Ran these algorithms on the 1000 genome dataset. ** Overall liked ddR since it was easy to implement the algorithms in the package. ** New ideas: *** Trying to separate the data layer from the execution layer *** Create an API that works on "chunks" (which is similar to the "parts" API in ddR). Would like to add these APIs to ddR. *** Indrajit: You should be able to get some of the chunk like features by using parts and dmapply. E.g., you can call dmapply to read 10 different files, which correspond 10 chunks now. These are however wrapped as a darray or dframe. But you can continue to work on the individual chunks by using parts(i). === 6/2/2016 === * Round table introduction * (Michael) Goals for the group: ** Make a common abstraction/interfaces to make it easier to work with distributed data and R ** Unify the interface ** Working group will run for a year. Get an API defined, get at least one open source reference implementations ** not everyone needs to work hands on. We will create smaller groups to focus on those aspects. ** We tried to get a diverse group of participants * Logistics: meet monthly, focus groups may meet more often * R Consoritum may be able to figure ways to fund smaller projects that come out of the working group * Michael Kane: Should we start with an inventory of what is available and people are using? ** Michael Lawrence: Yes, we should find the collection of tools as well as the use cases that are common. ** Joe: I will figure out a wiki space. * Javier: Who are the end users? Simon: Common layer needed to get algorithms working. We started from algos and tried to find the minimal common api. One of the goals is to make sure everyone is on the same page and not trying to create his/her own custom interface. * Javier: Should we try to get people with more algo expertise? * Joe: Simon do you have a stack diagram? * Simon: Can we get R Consortium to help write things up and draw things? * Next meeting: Javier is going to present SparkR next time. e75a4e3018cfffdaaa207761237224c2acd4dfa0 43 42 2016-10-18T11:29:29Z MichaelLawrence 9 September minutes wikitext text/x-wiki == Goals and Purpose == The Distributed Computing Working Group will endorse the design of a common abstraction for distributed data structures in R. We aim to have at least one open-source implementation, as well as a SQL implementation, released within a year of forming the group. == Members == * '''Michael Lawrence''' (Genentech) * '''Indrajit Roy''' (HP Enterprise) * ''Joe Rickert'' (Microsoft) * Bernd Bischl (LMU) * Matt Dowle (H2O) * Mario Inchiosa (Microsoft) * Michael Kane (Yale) * Javier Luraschi (RStudio) * Edward Ma (HP Enterprise) * Luke Tierney (University of Iowa) * Simon Urbanek (AT&T) == Minutes == === 9/8/2016 === ''Detailed notes were not taken for this meeting'' * Clark Fitzgerald: internship report ** Developed two packages for low-level Spark integration: rddlist, sparklite ** Patched a bug in Spark ** ddR needs refactoring before Spark integration is feasible: *** dlist, dframe, and darray should be formal classes. *** Partitions of data should be represented by a distributed list abstraction, and most functions (e.g., dmapply) should be implemented on top of that list. * Javier: sparklyr update ** Preparing for CRAN release ** Mario: what happened to sparkapi? *** Javier: sparkapi has been merged into sparklyr in order to avoid overhead of maintaining two packages. ddR can do everything it needs with sparklyr. * Luke Tierney: Update on the low-level vector abstraction, which might support interfaces like ddR and sparklyr. ** Overall approach seems feasible, but still working out a few details. ** Will land in a branch soon. * Bernd Bischl: update on the batchtools package ** Successor to BatchJobs based on in-memory database === 8/11/2016 === ''Meeting was canceled due to lack of availability.'' === 7/14/2016 === * Introduced Clark who is the intern funded by R Consortium. Clark is a graduate student from UC Davis. He will work on ddR integration with Spark and improving the core ddR API as well such as adding a distributed apply() for matrices, split function, etc. * Bernd: Can I play around with ddR now? What backend should I use? How robust is the code? ** Clark: It's in good enough shape to be played around with. We will continue to improve it. Hopefully the spark integration will be done before the end of my internship in September. * Q: Is anyone working on using ddR to make ML scale better. ** Indrajit: We have kmeans, glm, etc. already in CRAN. ** Michael Kane: We are working on glmnet and other packages related to algorithm development. * Javier gave a demo of sparklyr and sparkapi. ** Motivation for the pacakage: The SparkR package overrides the dplyr interface. This is an issue for RStudio. SparkR is not a CRAN package which makes it difficult to add changes. dplyr is the most popular tool by RStudio and is broken on SparkR. ** Sparklyr provides a dplyr interface. It will also support ML like interfaces, such as consuming a ML model. ** Sparklyr does not currently support any distributed computing features. Instead we can recommend ddR as the distributed computing framework on top of sparkapi. We will put the code in CRAN in a couple of weeks. ** Simon: Can you talk more about the wrapper/low level API to work with Spark? *** Javier: The package underneath the cover is called "sparkapi" it is to be used by pacakge builders. "spark_context()" and "invoke()" are the functionality to call scala methods. It does not you to currently run R user defined functions. I am currently working on enabling that feature. Depending upon the interest in using ddR with sparkapi, I can spend more time to make sparkapi feature rich. ** Indrajit: What versions of Spark are supported *** Javier: Anything after 1.6 ** Bernd: How do you export data? *** Javier: We are using all the code from SparkR. So everything in SparkR should continue to work. We don't need to change SparkR. We just need to maintain compatibility. ** Bernd: What happens when the RDDs are very large? *** Javier: Spark will spill on disk. * Michael Kane: Presented examples that he implemented on ddR. ** Talked about how the different distributed packages compare to each other in terms of functionality. ** Michael K. looked at glm and truncated SVD on ddR. Was able to implement irls on ddR by implementing two distributed functions such as "cross". In truncated SVD only needed to overload two different distributed multiplications. ** Ran these algorithms on the 1000 genome dataset. ** Overall liked ddR since it was easy to implement the algorithms in the package. ** New ideas: *** Trying to separate the data layer from the execution layer *** Create an API that works on "chunks" (which is similar to the "parts" API in ddR). Would like to add these APIs to ddR. *** Indrajit: You should be able to get some of the chunk like features by using parts and dmapply. E.g., you can call dmapply to read 10 different files, which correspond 10 chunks now. These are however wrapped as a darray or dframe. But you can continue to work on the individual chunks by using parts(i). === 6/2/2016 === * Round table introduction * (Michael) Goals for the group: ** Make a common abstraction/interfaces to make it easier to work with distributed data and R ** Unify the interface ** Working group will run for a year. Get an API defined, get at least one open source reference implementations ** not everyone needs to work hands on. We will create smaller groups to focus on those aspects. ** We tried to get a diverse group of participants * Logistics: meet monthly, focus groups may meet more often * R Consoritum may be able to figure ways to fund smaller projects that come out of the working group * Michael Kane: Should we start with an inventory of what is available and people are using? ** Michael Lawrence: Yes, we should find the collection of tools as well as the use cases that are common. ** Joe: I will figure out a wiki space. * Javier: Who are the end users? Simon: Common layer needed to get algorithms working. We started from algos and tried to find the minimal common api. One of the goals is to make sure everyone is on the same page and not trying to create his/her own custom interface. * Javier: Should we try to get people with more algo expertise? * Joe: Simon do you have a stack diagram? * Simon: Can we get R Consortium to help write things up and draw things? * Next meeting: Javier is going to present SparkR next time. c23b1adf726b3deb911daffdd3961b67022d7531 42 37 2016-10-18T11:11:44Z MichaelLawrence 9 add July minutes wikitext text/x-wiki == Goals and Purpose == The Distributed Computing Working Group will endorse the design of a common abstraction for distributed data structures in R. We aim to have at least one open-source implementation, as well as a SQL implementation, released within a year of forming the group. == Members == * '''Michael Lawrence''' (Genentech) * '''Indrajit Roy''' (HP Enterprise) * ''Joe Rickert'' (Microsoft) * Bernd Bischl (LMU) * Matt Dowle (H2O) * Mario Inchiosa (Microsoft) * Michael Kane (Yale) * Javier Luraschi (RStudio) * Edward Ma (HP Enterprise) * Luke Tierney (University of Iowa) * Simon Urbanek (AT&T) == Minutes == === 7/14/2016 === * Introduced Clark who is the intern funded by R Consortium. Clark is a graduate student from UC Davis. He will work on ddR integration with Spark and improving the core ddR API as well such as adding a distributed apply() for matrices, split function, etc. * Bernd: Can I play around with ddR now? What backend should I use? How robust is the code? ** Clark: It's in good enough shape to be played around with. We will continue to improve it. Hopefully the spark integration will be done before the end of my internship in September. * Q: Is anyone working on using ddR to make ML scale better. ** Indrajit: We have kmeans, glm, etc. already in CRAN. ** Michael Kane: We are working on glmnet and other packages related to algorithm development. * Javier gave a demo of sparklyr and sparkapi. ** Motivation for the pacakage: The SparkR package overrides the dplyr interface. This is an issue for RStudio. SparkR is not a CRAN package which makes it difficult to add changes. dplyr is the most popular tool by RStudio and is broken on SparkR. ** Sparklyr provides a dplyr interface. It will also support ML like interfaces, such as consuming a ML model. ** Sparklyr does not currently support any distributed computing features. Instead we can recommend ddR as the distributed computing framework on top of sparkapi. We will put the code in CRAN in a couple of weeks. ** Simon: Can you talk more about the wrapper/low level API to work with Spark? *** Javier: The package underneath the cover is called "sparkapi" it is to be used by pacakge builders. "spark_context()" and "invoke()" are the functionality to call scala methods. It does not you to currently run R user defined functions. I am currently working on enabling that feature. Depending upon the interest in using ddR with sparkapi, I can spend more time to make sparkapi feature rich. ** Indrajit: What versions of Spark are supported *** Javier: Anything after 1.6 ** Bernd: How do you export data? *** Javier: We are using all the code from SparkR. So everything in SparkR should continue to work. We don't need to change SparkR. We just need to maintain compatibility. ** Bernd: What happens when the RDDs are very large? *** Javier: Spark will spill on disk. * Michael Kane: Presented examples that he implemented on ddR. ** Talked about how the different distributed packages compare to each other in terms of functionality. ** Michael K. looked at glm and truncated SVD on ddR. Was able to implement irls on ddR by implementing two distributed functions such as "cross". In truncated SVD only needed to overload two different distributed multiplications. ** Ran these algorithms on the 1000 genome dataset. ** Overall liked ddR since it was easy to implement the algorithms in the package. ** New ideas: *** Trying to separate the data layer from the execution layer *** Create an API that works on "chunks" (which is similar to the "parts" API in ddR). Would like to add these APIs to ddR. *** Indrajit: You should be able to get some of the chunk like features by using parts and dmapply. E.g., you can call dmapply to read 10 different files, which correspond 10 chunks now. These are however wrapped as a darray or dframe. But you can continue to work on the individual chunks by using parts(i). === 6/2/2016 === * Round table introduction * (Michael) Goals for the group: ** Make a common abstraction/interfaces to make it easier to work with distributed data and R ** Unify the interface ** Working group will run for a year. Get an API defined, get at least one open source reference implementations ** not everyone needs to work hands on. We will create smaller groups to focus on those aspects. ** We tried to get a diverse group of participants * Logistics: meet monthly, focus groups may meet more often * R Consoritum may be able to figure ways to fund smaller projects that come out of the working group * Michael Kane: Should we start with an inventory of what is available and people are using? ** Michael Lawrence: Yes, we should find the collection of tools as well as the use cases that are common. ** Joe: I will figure out a wiki space. * Javier: Who are the end users? Simon: Common layer needed to get algorithms working. We started from algos and tried to find the minimal common api. One of the goals is to make sure everyone is on the same page and not trying to create his/her own custom interface. * Javier: Should we try to get people with more algo expertise? * Joe: Simon do you have a stack diagram? * Simon: Can we get R Consortium to help write things up and draw things? * Next meeting: Javier is going to present SparkR next time. 1bb85eab7653337c88d58dea328a48eddba9a454 37 31 2016-07-14T12:39:43Z MichaelLawrence 9 wikitext text/x-wiki == Goals and Purpose == The Distributed Computing Working Group will endorse the design of a common abstraction for distributed data structures in R. We aim to have at least one open-source implementation, as well as a SQL implementation, released within a year of forming the group. == Members == * '''Michael Lawrence''' (Genentech) * '''Indrajit Roy''' (HP Enterprise) * ''Joe Rickert'' (Microsoft) * Bernd Bischl (LMU) * Matt Dowle (H2O) * Mario Inchiosa (Microsoft) * Michael Kane (Yale) * Javier Luraschi (RStudio) * Edward Ma (HP Enterprise) * Luke Tierney (University of Iowa) * Simon Urbanek (AT&T) == Minutes == === 6/2/2016 === * Round table introduction * (Michael) Goals for the group: ** Make a common abstraction/interfaces to make it easier to work with distributed data and R ** Unify the interface ** Working group will run for a year. Get an API defined, get at least one open source reference implementations ** not everyone needs to work hands on. We will create smaller groups to focus on those aspects. ** We tried to get a diverse group of participants * Logistics: meet monthly, focus groups may meet more often * R Consoritum may be able to figure ways to fund smaller projects that come out of the working group * Michael Kane: Should we start with an inventory of what is available and people are using? ** Michael Lawrence: Yes, we should find the collection of tools as well as the use cases that are common. ** Joe: I will figure out a wiki space. * Javier: Who are the end users? Simon: Common layer needed to get algorithms working. We started from algos and tried to find the minimal common api. One of the goals is to make sure everyone is on the same page and not trying to create his/her own custom interface. * Javier: Should we try to get people with more algo expertise? * Joe: Simon do you have a stack diagram? * Simon: Can we get R Consortium to help write things up and draw things? * Next meeting: Javier is going to present SparkR next time. b3128649b3de00811e1cd9123a658da998b0c052 31 30 2016-06-24T15:44:58Z MichaelLawrence 9 wikitext text/x-wiki == Goals and Purpose == The Distributed Computing Working Group will endorse the design of a common abstraction for distributed data structures in R. We aim to have at least one open-source implementation, as well as a SQL implementation, released within a year of forming the group. == Members == * '''Michael Lawrence''' (Genentech) * '''Indrajit Roy''' (HP Enterprise) * ''Joe Rickert'' (Microsoft) * Bernd Bischl (LMU) * Matt Dowle (H2O) * Mario Inchiosa (Microsoft) * Michael Kane (Yale) * Javier Luraschi (RStudio) * Edward Ma (HP Enterprise) * Luke Tierney (University of Iowa) * Simon Urbanek (AT&T) == Minutes == * Round table introduction * (Michael) Goals for the group: ** Make a common abstraction/interfaces to make it easier to work with distributed data and R ** Unify the interface ** Working group will run for a year. Get an API defined, get at least one open source reference implementations ** not everyone needs to work hands on. We will create smaller groups to focus on those aspects. ** We tried to get a diverse group of participants * Logistics: meet monthly, focus groups may meet more often * R Consoritum may be able to figure ways to fund smaller projects that come out of the working group * Michael Kane: Should we start with an inventory of what is available and people are using? ** Michael Lawrence: Yes, we should find the collection of tools as well as the use cases that are common. ** Joe: I will figure out a wiki space. * Javier: Who are the end users? Simon: Common layer needed to get algorithms working. We started from algos and tried to find the minimal common api. One of the goals is to make sure everyone is on the same page and not trying to create his/her own custom interface. * Javier: Should we try to get people with more algo expertise? * Joe: Simon do you have a stack diagram? * Simon: Can we get R Consortium to help write things up and draw things? * Next meeting: Javier is going to present SparkR next time. bf7b25e31a3f2275af974298083a622944caebf3 30 29 2016-06-24T15:43:59Z MichaelLawrence 9 wikitext text/x-wiki == Goals and Purpose == The Distributed Computing Working Group will endorse the design of a common abstraction for distributed data structures in R. We aim to have at least one open-source implementation, as well as a SQL implementation, released within a year of forming the group. == Members == * '''Michael Lawrence''' (Genentech) * '''Indrajit Roy''' (HP Enterprise) * ''Joe Rickert'' (Microsoft) * Bernd Bischl (LMU) * Matt Dowle (H2O) * Mario Inchiosa (Microsoft) * Michael Kane (Yale) * Javier Luraschi (RStudio) * Edward Ma (HP Enterprise) * Luke Tierney (University of Iowa) * Simon Urbanek (AT&T) == Minutes == * Round table introduction * (Michael) Goals for the group: * Make a common abstraction/interfaces to make it easier to work with distributed data and R * Unify the interface * Working group will run for a year. Get an API defined, get at least one open source reference implementations * not everyone needs to work hands on. We will create smaller groups to focus on those aspects. * We tried to get a diverse group of participants * Logistics: meet monthly, focus groups may meet more often * R Consoritum may be able to figure ways to fund smaller projects that come out of the working group * Michael Kane: Should we start with an inventory of what is available and people are using? * Michael Lawrence: Yes, we should find the collection of tools as well as the use cases that are common. * Joe: I will figure out a wiki space. * javier: Who are the end users? Simon: Common layer needed to get algorithms working. We started from algos and tried to find the minimal common api. One of the goals is to make sure everyone is on the same page and not trying to create his/her own custom interface. * Javier: Should we try to get people with more algo expertise? * Joe: Simon do you have a stack diagram? * Simon: Can we get R Consortium to help write things up and draw things? * Next meeting: Javier is going to present SparkR next time. 7215e495df16c72053fd93bb9f238767d59067c6 29 28 2016-06-24T15:34:25Z MichaelLawrence 9 wikitext text/x-wiki Goals and Purpose The Distributed Computing Working Group will endorse the design of a common abstraction for distributed data structures in R. We aim to have at least one open-source implementation, as well as a SQL implementation, released within a year of forming the group. Members * '''Michael Lawrence''' (Genentech) * '''Indrajit Roy''' (HP Enterprise) * ''Joe Rickert'' (Microsoft) * Bernd Bischl (LMU) * Matt Dowle (H2O) * Mario Inchiosa (Microsoft) * Michael Kane (Yale) * Javier Luraschi (RStudio) * Edward Ma (HP Enterprise) * Luke Tierney (University of Iowa) * Simon Urbanek (AT&T) Minutes f0e7c7e7ffbe41a37b1e96736e910acb86c75653 28 27 2016-06-24T15:34:06Z MichaelLawrence 9 wikitext text/x-wiki Goals and Purpose The Distributed Computing Working Group will endorse the design of a common abstraction for distributed data structures in R. We aim to have at least one open-source implementation, as well as a SQL implementation, released within a year of forming the group. Members * '''Michael Lawrence''' (Genentech) * ''''Indrajit Roy'''' (HP Enterprise) * ''Joe Rickert'' (Microsoft) * Bernd Bischl (LMU) * Matt Dowle (H2O) * Mario Inchiosa (Microsoft) * Michael Kane (Yale) * Javier Luraschi (RStudio) * Edward Ma (HP Enterprise) * Luke Tierney (University of Iowa) * Simon Urbanek (AT&T) Minutes ac38889114e3c83c6f95927ec76f18a2e3031afa 27 26 2016-06-24T15:33:49Z MichaelLawrence 9 wikitext text/x-wiki Goals and Purpose The Distributed Computing Working Group will endorse the design of a common abstraction for distributed data structures in R. We aim to have at least one open-source implementation, as well as a SQL implementation, released within a year of forming the group. Members * '''Michael Lawrence''' (Genentech) * ""Indrajit Roy"" (HP Enterprise) * ''Joe Rickert'' (Microsoft) * Bernd Bischl (LMU) * Matt Dowle (H2O) * Mario Inchiosa (Microsoft) * Michael Kane (Yale) * Javier Luraschi (RStudio) * Edward Ma (HP Enterprise) * Luke Tierney (University of Iowa) * Simon Urbanek (AT&T) Minutes 0e4c28f4d3b9f5d438e9dd1001f5bb822d0e453e 26 2016-06-24T15:32:47Z MichaelLawrence 9 Created page with "Goals and Purpose The Distributed Computing Working Group will endorse the design of a common abstraction for distributed data structures in R. We aim to have at least one op..." wikitext text/x-wiki Goals and Purpose The Distributed Computing Working Group will endorse the design of a common abstraction for distributed data structures in R. We aim to have at least one open-source implementation, as well as a SQL implementation, released within a year of forming the group. Members '''Michael Lawrence''' (Genentech) ""Indrajit Roy"" (HP Enterprise) ''Joe Rickert'' (Microsoft) Bernd Bischl () Matt Dowle (H2O) Mario Inchiosa (Microsoft) Michael Kane (Yale) Javier Luraschi (RStudio) Edward Ma (HP Enterprise) Luke Tierney (University of Iowa) Simon Urbanek (AT&T) Minutes 7f8541705d0d5ef1f40cc3072a7084f5c4cc77fd 0 20 76 75 2017-05-01T18:45:03Z MichaelLawrence 9 wikitext text/x-wiki Authors: Michael Lawrence and Indrajit Roy == Introduction == Data sizes continue to increase, while single core performance has stagnated. We scale computations by leveraging multiple cores and machines. Large datasets are expensive to replicate, so we minimize data movement by moving the computation to the data. Many systems, such as Hadoop, Spark, and massively parallel processing (MPP) databases, have emerged to support these strategies, and each exposes its own unique interface, with little standardization. Developing and executing an algorithm in the distributed context is a complex task that requires specific knowledge of and dependency on the system storing the data. It is also a task orthogonal to the primary role of a data scientist or statistician: extracting knowledge from data. The task thus falls to the data analysis environment, which should mask the complexity behind a familiar interface, maintaining user productivity. However, it is not always feasible to automatically determine the optimal strategy for a given problem, so user input is often beneficial. The environment should only abstract the details to the extent deemed appropriate by the user. R needs a standardized, layered and idiomatic abstraction for computing on distributed data structures. R has many packages that provide parallelism constructs as well as bridges to distributed systems such as Hadoop. Unfortunately, each interface has its own syntax, parallelism techniques, and supported platform(s). As a consequence, contributors are forced to learn multiple idiosyncratic interfaces, and to restrict each implementation to a particular interface, thus limiting the applicability and adoption of their software and hampering interoperability. The idea of a unified interface stemmed from a cross-industry workshop organized at HP Labs in early 2015. The workshop was attended by different companies, universities, and R-core members. Immediately after the workshop, Indrajit Roy, Edward Ma, and Michael Lawrence began designing an abstraction that later became known as the CRAN package ddR (Distributed Data in R)[1]. It declares a unified API for distributed computing in R and ensures that R programs written using the API are portable across different systems, such as Distributed R, Spark, etc. The ddR package has completed its initial phase of development; the first release is now on CRAN. Three ddR machine-learning algorithms are also on CRAN, randomForest.ddR, glm.ddR, and kmeans.ddR. Two reference backends for ddR have been completed, one for R’s parallel package, and one for HP Distributed R. Example code and scripts to run algorithms and code on both of these backends are available in our public repository at https://github.com/vertica/ddR. The overarching goal of the ddR project was for it to be a starting point in a collaborative effort, ultimately leading to a standard API for working with distributed data in R. We decided that it was natural for the R Consortium to sponsor the collaboration, as it should involve both industry and R-core members. To this end, we established the R Consortium Working Group on Distributed Computing, with a planned duration of a single year and the following aims: # Agree on the goal of the group, i.e., we should have a unifying framework for distributed computing. Define success metric. # Brainstorm on what primitives should be included in the API. We can use ddR’s API of distributed data-structures and dmapply as the starting proposal. Understand relationship with existing packages such as parallel, foreach, etc. # Explore how ddR like interface will interact with databases. Are there connections or redundancies with dplyr and multiplyr? # Decide on a reference implementation for the API. # Decide on whether we should also implement a few ecosystem packages, e.g., distributed algorithms written using the API. We declared the following milestones: # Mid-year milestone: Finalize API. Decide who all will help with developing the top-level implementation and backends. # End-year milestone: Summary report and reference implementation. Socialize the final package. This report outlines the progress we have made on the above goals and milestones, and how we plan to continue progress in the second half of the working group term. == Results and Current Status == The working group has achieved the first goal by agreeing that we should aim for a unifying distributed computing abstraction, and we have treated ddR as an informal API proposal. We have discussed many of the issues related to the second goal, deciding which primitives should be part of the API. We aim for the API to support three shapes of data --- lists, arrays and data frames --- and to enable the loading and basic manipulation of distributed data, including multiple modes of functional iteration (e.g., apply() operations). We aim to preserve consistency with base R data structures and functions, so as to provide a simple path for users to port computations to distributed systems. The ddR constructs permit a user to express a wide variety of applications, including machine-learning algorithms, that will run on different backends. We have successfully implemented distributed versions of algorithms such as K-means, Regression, Random Forest, and PageRank using the ddR API. Some of these ddR algorithms are now available on CRAN. In addition, the package provides several generic definitions of common operators (such as colSums) that can be invoked on distributed objects residing in the supporting backends. Each custom ddR backend is encapsulated in its own driver package. In the conventional style of functional OOP, the driver registers methods for generics declared by the backend API, such that ddR can dispatch the backend-specific instructions by only calling the generics. The working group explored potential new backends with the aim of broadening the applicability of the ddR interface. We hosted presentations from external speakers on Spark and TensorFlow, and also considered a generic SQL backend. The discussion focused on Spark integration, and the R Consortium-funded intern Clark Fitzgerald took on the task of developing a prototype Spark backend. The development of the Spark backend encountered some obstacles, including the immaturity of Spark and its R interfaces. Development is currently paused, as we await additional funding. During the monthly meetings, the working group deliberated on different design improvements for ddR itself. We list two key topics that were discussed. First, Michael Kane and Bryan Lewis argued for a lower level API that directly operates on chunks of data. While ddR supports chunk-wise data processing, via a combination of dmapply() and parts(), its focus on distributed data structures means that the chunk-based processing is exposed as the manipulation of these data structures. Second, Clark Fitzgerald proposed restructuring the ddR code into two layers that includes chunk-wise processing while retaining the emphasis on distributed data structures[2]. The lower level API, which will interface with backends, will use a Map() like primitive to evaluate functions on chunks of data, while the higher level ddR API will expose distributed data structures, dmapply, and other convenience functions. This refactoring would facilitate the implementation of additional backends. == Discussion and Future Plans == The R Consortium-funded working group and internship has helped us start a conversation on distributed computing APIs for R. The ddR CRAN package is a concrete outcome of this working group, and serves as a platform for exploring APIs and their integration with different backends. While ddR is still maturing, we have arrived at a consensus for how we should improve and finalize the ddR API. As part of our goal for a reference implementation, we aim to develop one or more prototype backends that will make the ddR interface useful in practice. A good candidate backend is any open-source system that is effective at R use cases and has strong community support. Spark remains a viable candidate, and we also aim to further explore TensorFlow. We plan for a second intern to perform three tasks: (1) refactor the ddR API to a more final form, (2) compare Spark and TensorFlow in detail, with an eye towards the feasibility of implementing a useful backend, and (3) implement a prototype backend based on Spark or TensorFlow, depending on the recommendation of the working group. By the conclusion of the working group, it will have produced: * A stable version of the ddR package and at least one practical backend, released on CRAN, * A list of requirements that are relevant and of interest to the community but have not yet been met by ddR, including alternative implementations that remain independent, * A list of topics that the group believes worthy of further investigation. [1] http://h30507.www3.hp.com/t5/Behind-the-scenes-Labs/Enhancing-R-for-Distributed-Computing/ba-p/6795535#.VjE1K7erQQj [2] Clark Fitzgerald. https://github.com/vertica/ddR/wiki/Design dbf116b79c32c78d21cd69187da8e4097857e009 75 2017-05-01T18:43:10Z MichaelLawrence 9 Created page with "== Introduction == Data sizes continue to increase, while single core performance has stagnated. We scale computations by leveraging multiple cores and machines. Large datas..." wikitext text/x-wiki == Introduction == Data sizes continue to increase, while single core performance has stagnated. We scale computations by leveraging multiple cores and machines. Large datasets are expensive to replicate, so we minimize data movement by moving the computation to the data. Many systems, such as Hadoop, Spark, and massively parallel processing (MPP) databases, have emerged to support these strategies, and each exposes its own unique interface, with little standardization. Developing and executing an algorithm in the distributed context is a complex task that requires specific knowledge of and dependency on the system storing the data. It is also a task orthogonal to the primary role of a data scientist or statistician: extracting knowledge from data. The task thus falls to the data analysis environment, which should mask the complexity behind a familiar interface, maintaining user productivity. However, it is not always feasible to automatically determine the optimal strategy for a given problem, so user input is often beneficial. The environment should only abstract the details to the extent deemed appropriate by the user. R needs a standardized, layered and idiomatic abstraction for computing on distributed data structures. R has many packages that provide parallelism constructs as well as bridges to distributed systems such as Hadoop. Unfortunately, each interface has its own syntax, parallelism techniques, and supported platform(s). As a consequence, contributors are forced to learn multiple idiosyncratic interfaces, and to restrict each implementation to a particular interface, thus limiting the applicability and adoption of their software and hampering interoperability. The idea of a unified interface stemmed from a cross-industry workshop organized at HP Labs in early 2015. The workshop was attended by different companies, universities, and R-core members. Immediately after the workshop, Indrajit Roy, Edward Ma, and Michael Lawrence began designing an abstraction that later became known as the CRAN package ddR (Distributed Data in R)[1]. It declares a unified API for distributed computing in R and ensures that R programs written using the API are portable across different systems, such as Distributed R, Spark, etc. The ddR package has completed its initial phase of development; the first release is now on CRAN. Three ddR machine-learning algorithms are also on CRAN, randomForest.ddR, glm.ddR, and kmeans.ddR. Two reference backends for ddR have been completed, one for R’s parallel package, and one for HP Distributed R. Example code and scripts to run algorithms and code on both of these backends are available in our public repository at https://github.com/vertica/ddR. The overarching goal of the ddR project was for it to be a starting point in a collaborative effort, ultimately leading to a standard API for working with distributed data in R. We decided that it was natural for the R Consortium to sponsor the collaboration, as it should involve both industry and R-core members. To this end, we established the R Consortium Working Group on Distributed Computing, with a planned duration of a single year and the following aims: # Agree on the goal of the group, i.e., we should have a unifying framework for distributed computing. Define success metric. # Brainstorm on what primitives should be included in the API. We can use ddR’s API of distributed data-structures and dmapply as the starting proposal. Understand relationship with existing packages such as parallel, foreach, etc. # Explore how ddR like interface will interact with databases. Are there connections or redundancies with dplyr and multiplyr? # Decide on a reference implementation for the API. # Decide on whether we should also implement a few ecosystem packages, e.g., distributed algorithms written using the API. We declared the following milestones: # Mid-year milestone: Finalize API. Decide who all will help with developing the top-level implementation and backends. # End-year milestone: Summary report and reference implementation. Socialize the final package. This report outlines the progress we have made on the above goals and milestones, and how we plan to continue progress in the second half of the working group term. == Results and Current Status == The working group has achieved the first goal by agreeing that we should aim for a unifying distributed computing abstraction, and we have treated ddR as an informal API proposal. We have discussed many of the issues related to the second goal, deciding which primitives should be part of the API. We aim for the API to support three shapes of data --- lists, arrays and data frames --- and to enable the loading and basic manipulation of distributed data, including multiple modes of functional iteration (e.g., apply() operations). We aim to preserve consistency with base R data structures and functions, so as to provide a simple path for users to port computations to distributed systems. The ddR constructs permit a user to express a wide variety of applications, including machine-learning algorithms, that will run on different backends. We have successfully implemented distributed versions of algorithms such as K-means, Regression, Random Forest, and PageRank using the ddR API. Some of these ddR algorithms are now available on CRAN. In addition, the package provides several generic definitions of common operators (such as colSums) that can be invoked on distributed objects residing in the supporting backends. Each custom ddR backend is encapsulated in its own driver package. In the conventional style of functional OOP, the driver registers methods for generics declared by the backend API, such that ddR can dispatch the backend-specific instructions by only calling the generics. The working group explored potential new backends with the aim of broadening the applicability of the ddR interface. We hosted presentations from external speakers on Spark and TensorFlow, and also considered a generic SQL backend. The discussion focused on Spark integration, and the R Consortium-funded intern Clark Fitzgerald took on the task of developing a prototype Spark backend. The development of the Spark backend encountered some obstacles, including the immaturity of Spark and its R interfaces. Development is currently paused, as we await additional funding. During the monthly meetings, the working group deliberated on different design improvements for ddR itself. We list two key topics that were discussed. First, Michael Kane and Bryan Lewis argued for a lower level API that directly operates on chunks of data. While ddR supports chunk-wise data processing, via a combination of dmapply() and parts(), its focus on distributed data structures means that the chunk-based processing is exposed as the manipulation of these data structures. Second, Clark Fitzgerald proposed restructuring the ddR code into two layers that includes chunk-wise processing while retaining the emphasis on distributed data structures[2]. The lower level API, which will interface with backends, will use a Map() like primitive to evaluate functions on chunks of data, while the higher level ddR API will expose distributed data structures, dmapply, and other convenience functions. This refactoring would facilitate the implementation of additional backends. == Discussion and Future Plans == The R Consortium-funded working group and internship has helped us start a conversation on distributed computing APIs for R. The ddR CRAN package is a concrete outcome of this working group, and serves as a platform for exploring APIs and their integration with different backends. While ddR is still maturing, we have arrived at a consensus for how we should improve and finalize the ddR API. As part of our goal for a reference implementation, we aim to develop one or more prototype backends that will make the ddR interface useful in practice. A good candidate backend is any open-source system that is effective at R use cases and has strong community support. Spark remains a viable candidate, and we also aim to further explore TensorFlow. We plan for a second intern to perform three tasks: (1) refactor the ddR API to a more final form, (2) compare Spark and TensorFlow in detail, with an eye towards the feasibility of implementing a useful backend, and (3) implement a prototype backend based on Spark or TensorFlow, depending on the recommendation of the working group. By the conclusion of the working group, it will have produced: * A stable version of the ddR package and at least one practical backend, released on CRAN, * A list of requirements that are relevant and of interest to the community but have not yet been met by ddR, including alternative implementations that remain independent, * A list of topics that the group believes worthy of further investigation. [1] http://h30507.www3.hp.com/t5/Behind-the-scenes-Labs/Enhancing-R-for-Distributed-Computing/ba-p/6795535#.VjE1K7erQQj [2] Clark Fitzgerald. https://github.com/vertica/ddR/wiki/Design 811764c2232c8fd884213a53ed52481ab9e47f47 0 11 22 19 2016-06-20T15:27:23Z Lukasstadler 8 wikitext text/x-wiki Using a small Java tool that drives the gcc preprocessor and runs some specialized regex queries yields the following reports on actual usage of the macros, typedefs, variables and functions defined in various R header files. The usage counts were determine by scanning all CRAN source files (the CRAN dump is from mid-March 2016). They may include false positives because this was done on a textual base only. The packages "SOD" and "Boom" were ignored because they contain many false positives. * [[Native API stats of R.h]] * [[Native API stats of Rinternals.h without USE_R_INTERNALS]] * [[Native API stats of Rinternals.h with USE_R_INTERNALS]] * [[Native API stats of all header files]] I am not a lawyer, but please be aware that this is derived from the include files and may be covered by the same licenses, e.g.: <pre> /* * R : A Computer Language for Statistical Data Analysis * Copyright (C) 1995, 1996 Robert Gentleman and Ross Ihaka * Copyright (C) 1999-2015 The R Core Team. * * This program is free software; you can redistribute it and/or modify * it under the terms of the GNU Lesser General Public License as published by * the Free Software Foundation; either version 2.1 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU Lesser General Public License for more details. * * You should have received a copy of the GNU Lesser General Public License * along with this program; if not, a copy is available at * https://www.R-project.org/Licenses/ */ /* * R : A Computer Language for Statistical Data Analysis * Copyright (C) 2006-2016 The R Core Team. * * This program is free software; you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation; either version 2 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program; if not, a copy is available at * https://www.R-project.org/Licenses/ */ </pre> 9caf0bd42520bcc4673da1d79c50314da522f684 19 2016-06-20T15:21:22Z Lukasstadler 8 Created page with "Using a small Java tool that drives the gcc preprocessor and runs some specialized regex queries yields the following reports on actual usage of the macros, typedefs, variable..." wikitext text/x-wiki Using a small Java tool that drives the gcc preprocessor and runs some specialized regex queries yields the following reports on actual usage of the macros, typedefs, variables and functions defined in various R header files: The usage counts were determine by scanning all CRAN source files, they may include false positives because this was done on a textual base only. The packages "SOD" and "Boom" were ignored because they contain many false positives. * [[Native API stats of R.h]] * [[Native API stats of Rinternals.h without USE_R_INTERNALS]] * [[Native API stats of Rinternals.h with USE_R_INTERNALS]] * [[Native API stats of all header files]] I am not a lawyer, but please be aware that this is derived from the include files and may be covered by the same licenses, e.g.: <pre> /* * R : A Computer Language for Statistical Data Analysis * Copyright (C) 1995, 1996 Robert Gentleman and Ross Ihaka * Copyright (C) 1999-2015 The R Core Team. * * This program is free software; you can redistribute it and/or modify * it under the terms of the GNU Lesser General Public License as published by * the Free Software Foundation; either version 2.1 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU Lesser General Public License for more details. * * You should have received a copy of the GNU Lesser General Public License * along with this program; if not, a copy is available at * https://www.R-project.org/Licenses/ */ /* * R : A Computer Language for Statistical Data Analysis * Copyright (C) 2006-2016 The R Core Team. * * This program is free software; you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation; either version 2 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program; if not, a copy is available at * https://www.R-project.org/Licenses/ */ </pre> 4fc117224aa7abd7080ab7cbdc18cbff50adbc45 0 1 82 81 2017-11-28T17:22:03Z Jmertic 19 /* Get Involved */ wikitext text/x-wiki == Welcome to the R Consortium Wiki == The R Consortium, Inc. is a group organized under an open source governance and foundation model to provide support to the R community, the R Foundation and groups and individuals, using, maintaining and distributing R software. This wiki space is for working groups and ISC Project collaboration and documentation. == Get Involved == * [https://www.r-consortium.org/ R Consortium Website] * [https://www.r-consortium.org/projects/call-for-proposals Looking to Submit a Proposal] * [https://lists.r-consortium.org Sign up and follow along in mailing lists] * [https://twitter.com/RConsortium Follow us on Twitter] * [[R Consortium and the R Community Code of Conduct]] * [[Top Level Projects]] == Working Groups == * [[R Native API|Native APIs for R]] * [[Distributed Computing Working Group|Distributed Computing]] * [[Code Coverage Tool for R|Code Coverage for R]] * [[R Certification|R Certification]] * [[R in Medicine|R in Medicine]] * [[R in Pharma|R in Pharma]] '''''This wiki supports Linux Foundation ID single-sign-on and registration with the link at the top this page. Other components of the R Consortium that do not support single-sign-on will directly request your Linux Foundation ID username and password for login.''''' Consult the [//meta.wikimedia.org/wiki/Help:Contents User's Guide] for information on using the wiki software. 68b9e9aa500728b37b8c617fd2d30c1c3a761b5b 81 58 2017-11-08T15:54:04Z Jmertic 19 /* Working Groups */ wikitext text/x-wiki == Welcome to the R Consortium Wiki == The R Consortium, Inc. is a group organized under an open source governance and foundation model to provide support to the R community, the R Foundation and groups and individuals, using, maintaining and distributing R software. This wiki space is for working groups and ISC Project collaboration and documentation. == Get Involved == * [https://www.r-consortium.org/ R Consortium Website] * [https://www.r-consortium.org/projects/call-for-proposals Looking to Submit a Proposal] * [https://lists.r-consortium.org Sign up and follow along in mailing lists] * [https://twitter.com/RConsortium Follow us on Twitter] * [[R Consortium and the R Community Code of Conduct]] == Working Groups == * [[R Native API|Native APIs for R]] * [[Distributed Computing Working Group|Distributed Computing]] * [[Code Coverage Tool for R|Code Coverage for R]] * [[R Certification|R Certification]] * [[R in Medicine|R in Medicine]] * [[R in Pharma|R in Pharma]] '''''This wiki supports Linux Foundation ID single-sign-on and registration with the link at the top this page. Other components of the R Consortium that do not support single-sign-on will directly request your Linux Foundation ID username and password for login.''''' Consult the [//meta.wikimedia.org/wiki/Help:Contents User's Guide] for information on using the wiki software. 5d8cdc3ea434fb0a3a0e60faf6fb1b8d67e1ac33 58 50 2017-01-21T06:21:33Z MeharPratapSingh 15 /* Working Groups */ wikitext text/x-wiki == Welcome to the R Consortium Wiki == The R Consortium, Inc. is a group organized under an open source governance and foundation model to provide support to the R community, the R Foundation and groups and individuals, using, maintaining and distributing R software. This wiki space is for working groups and ISC Project collaboration and documentation. == Get Involved == * [https://www.r-consortium.org/ R Consortium Website] * [https://www.r-consortium.org/projects/call-for-proposals Looking to Submit a Proposal] * [https://lists.r-consortium.org Sign up and follow along in mailing lists] * [https://twitter.com/RConsortium Follow us on Twitter] * [[R Consortium and the R Community Code of Conduct]] == Working Groups == * [[R Native API|Native APIs for R]] * [[Distributed Computing Working Group|Distributed Computing]] * [[Code Coverage Tool for R|Code Coverage for R]] * [[R Certification|R Certification]] '''''This wiki supports Linux Foundation ID single-sign-on and registration with the link at the top this page. Other components of the R Consortium that do not support single-sign-on will directly request your Linux Foundation ID username and password for login.''''' Consult the [//meta.wikimedia.org/wiki/Help:Contents User's Guide] for information on using the wiki software. 86b5ccf35e381ca48489fdda5da6a8b359111257 50 41 2016-11-03T20:39:10Z Trishan 3 wikitext text/x-wiki == Welcome to the R Consortium Wiki == The R Consortium, Inc. is a group organized under an open source governance and foundation model to provide support to the R community, the R Foundation and groups and individuals, using, maintaining and distributing R software. This wiki space is for working groups and ISC Project collaboration and documentation. == Get Involved == * [https://www.r-consortium.org/ R Consortium Website] * [https://www.r-consortium.org/projects/call-for-proposals Looking to Submit a Proposal] * [https://lists.r-consortium.org Sign up and follow along in mailing lists] * [https://twitter.com/RConsortium Follow us on Twitter] * [[R Consortium and the R Community Code of Conduct]] == Working Groups == * [[R Native API|Native APIs for R]] * [[Distributed Computing Working Group|Distributed Computing]] * [[Code Coverage Tool for R|Code Coverage for R]] '''''This wiki supports Linux Foundation ID single-sign-on and registration with the link at the top this page. Other components of the R Consortium that do not support single-sign-on will directly request your Linux Foundation ID username and password for login.''''' Consult the [//meta.wikimedia.org/wiki/Help:Contents User's Guide] for information on using the wiki software. 84d1949291913b37c2062439d59e60e10c33c019 41 39 2016-09-21T15:45:29Z Benmarwick@gmail.com 11 /* Working Groups */ add code cov WG to list wikitext text/x-wiki == Welcome to the R Consortium Wiki == The R Consortium, Inc. is a group organized under an open source governance and foundation model to provide support to the R community, the R Foundation and groups and individuals, using, maintaining and distributing R software. This wiki space is for working groups and ISC Project collaboration and documentation. == Get Involved == * [https://www.r-consortium.org/ R Consortium Website] * [https://www.r-consortium.org/about/isc/proposals Looking to Submit a Proposal] * [https://twitter.com/RConsortium Follow us on Twitter] * [[R Consortium and the R Community Code of Conduct]] == Working Groups == * [[R Native API|Native APIs for R]] * [[Distributed Computing Working Group|Distributed Computing]] * [[Code Coverage Tool for R|Code Coverage for R]] '''''This wiki supports Linux Foundation ID single-sign-on and registration with the link at the top this page. Other components of the R Consortium that do not support single-sign-on will directly request your Linux Foundation ID username and password for login.''''' Consult the [//meta.wikimedia.org/wiki/Help:Contents User's Guide] for information on using the wiki software. d6669813fb12db5214a0b3798d79b75f8d87063b 39 38 2016-08-19T16:46:08Z Trishan 3 wikitext text/x-wiki == Welcome to the R Consortium Wiki == The R Consortium, Inc. is a group organized under an open source governance and foundation model to provide support to the R community, the R Foundation and groups and individuals, using, maintaining and distributing R software. This wiki space is for working groups and ISC Project collaboration and documentation. == Get Involved == * [https://www.r-consortium.org/ R Consortium Website] * [https://www.r-consortium.org/about/isc/proposals Looking to Submit a Proposal] * [https://twitter.com/RConsortium Follow us on Twitter] * [[R Consortium and the R Community Code of Conduct]] == Working Groups == * [[R Native API|Native APIs for R]] * [[Distributed Computing Working Group|Distributed Computing]] '''''This wiki supports Linux Foundation ID single-sign-on and registration with the link at the top this page. Other components of the R Consortium that do not support single-sign-on will directly request your Linux Foundation ID username and password for login.''''' Consult the [//meta.wikimedia.org/wiki/Help:Contents User's Guide] for information on using the wiki software. 1ebd0916c5e84563d4134b699e9f8343e6d6b479 38 25 2016-08-19T16:44:42Z Trishan 3 /* Get Involved */ wikitext text/x-wiki == Welcome to the R Consortium Wiki == The R Consortium, Inc. is a group organized under an open source governance and foundation model to provide support to the R community, the R Foundation and groups and individuals, using, maintaining and distributing R software. This wiki space is for working groups and ISC Project collaboration and documentation. == Get Involved == * [https://www.r-consortium.org/ R Consortium Website] * [https://www.r-consortium.org/about/isc/proposals Looking to Submit a Proposal] * [https://twitter.com/RConsortium Follow us on Twitter] * [R Consortium Code of Conduct] == Working Groups == * [[R Native API|Native APIs for R]] * [[Distributed Computing Working Group|Distributed Computing]] '''''This wiki supports Linux Foundation ID single-sign-on and registration with the link at the top this page. Other components of the R Consortium that do not support single-sign-on will directly request your Linux Foundation ID username and password for login.''''' Consult the [//meta.wikimedia.org/wiki/Help:Contents User's Guide] for information on using the wiki software. f0fabd3426466809edb1e5a8d62382b6965ecdd0 25 24 2016-06-24T15:22:57Z MichaelLawrence 9 wikitext text/x-wiki == Welcome to the R Consortium Wiki == The R Consortium, Inc. is a group organized under an open source governance and foundation model to provide support to the R community, the R Foundation and groups and individuals, using, maintaining and distributing R software. This wiki space is for working groups and ISC Project collaboration and documentation. == Get Involved == * [https://www.r-consortium.org/ R Consortium Website] * [https://www.r-consortium.org/about/isc/proposals Looking to Submit a Proposal] * [https://twitter.com/RConsortium Follow us on Twitter] == Working Groups == * [[R Native API|Native APIs for R]] * [[Distributed Computing Working Group|Distributed Computing]] '''''This wiki supports Linux Foundation ID single-sign-on and registration with the link at the top this page. Other components of the R Consortium that do not support single-sign-on will directly request your Linux Foundation ID username and password for login.''''' Consult the [//meta.wikimedia.org/wiki/Help:Contents User's Guide] for information on using the wiki software. 236a3b380deb9c0904a9267ca7aaddae74c0e7c6 24 11 2016-06-24T15:20:11Z MichaelLawrence 9 wikitext text/x-wiki == Welcome to the R Consortium Wiki == The R Consortium, Inc. is a group organized under an open source governance and foundation model to provide support to the R community, the R Foundation and groups and individuals, using, maintaining and distributing R software. This wiki space is for working groups and ISC Project collaboration and documentation. == Get Involved == * [https://www.r-consortium.org/ R Consortium Website] * [https://www.r-consortium.org/about/isc/proposals Looking to Submit a Proposal] * [https://twitter.com/RConsortium Follow us on Twitter] == Working Groups == * [[R Native API|Native APIs for R]] * [[Distributed Computing]] '''''This wiki supports Linux Foundation ID single-sign-on and registration with the link at the top this page. 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This wiki space is for working groups and ISC Project collaboration and documentation. == Get Involved == * [https://www.r-consortium.org/ R Consortium Website] * [https://www.r-consortium.org/about/isc/proposals Looking to Submit a Proposal] * [https://twitter.com/RConsortium Follow us on Twitter] == Working Groups == * [[R Native API|Native APIs for R]] '''''This wiki supports Linux Foundation ID single-sign-on and registration with the link at the top this page. Other components of the R Consortium that do not support single-sign-on will directly request your Linux Foundation ID username and password for login.''''' Consult the [//meta.wikimedia.org/wiki/Help:Contents User's Guide] for information on using the wiki software. a6afd9317417196480349ae68406c8c507d21aab 5 4 2016-03-08T17:41:04Z Trishan 3 wikitext text/x-wiki == Welcome to the R Consortium Wiki == The R Consortium, Inc. is a group organized under an open source governance and foundation model to provide support to the R community, the R Foundation and groups and individuals, using, maintaining and distributing R software. This wiki space is for working groups and ISC Project collaboration and documentation. == Get Involved == * [https://www.r-consortium.org/ R Consortium Website] * [https://www.r-consortium.org/about/isc/proposals Looking to Submit a Proposal] * [https://twitter.com/RConsortium Follow us on Twitter] '''''This wiki supports Linux Foundation ID single-sign-on and registration with the link at the top this page. Other components of the R Consortium that do not support single-sign-on will directly request your Linux Foundation ID username and password for login.''''' Consult the [//meta.wikimedia.org/wiki/Help:Contents User's Guide] for information on using the wiki software. 5f91552aced856a95027e053b007254cb487ec72 4 3 2016-03-08T17:40:46Z Trishan 3 wikitext text/x-wiki <strong>MediaWiki has been successfully installed.</strong> == Welcome to the R Consortium Wiki == The R Consortium, Inc. is a group organized under an open source governance and foundation model to provide support to the R community, the R Foundation and groups and individuals, using, maintaining and distributing R software. This wiki space is for working groups and ISC Project collaboration and documentation. == Get Involved == * [https://www.r-consortium.org/ R Consortium Website] * [https://www.r-consortium.org/about/isc/proposals Looking to Submit a Proposal] * [https://twitter.com/RConsortium Follow us on Twitter] '''''This wiki supports Linux Foundation ID single-sign-on and registration with the link at the top this page. Other components of the R Consortium that do not support single-sign-on will directly request your Linux Foundation ID username and password for login.''''' Consult the [//meta.wikimedia.org/wiki/Help:Contents User's Guide] for information on using the wiki software. 0e3fb0ac2038d7de5a27e3452d4b0e8e7e981937 3 1 2016-03-08T17:40:16Z Trishan 3 wikitext text/x-wiki <strong>MediaWiki has been successfully installed.</strong> == Welcome to the R Consortium Wiki == The R Consortium, Inc. is a group organized under an open source governance and foundation model to provide support to the R community, the R Foundation and groups and individuals, using, maintaining and distributing R software. This wiki space is for working groups and ISC Project collaboration and documentation. == Get Involved == * [https://www.r-consortium.org/ R Consortium Website] * [https://www.r-consortium.org/about/isc/proposals Looking to Submit a Proposal] * [https://twitter.com/RConsortium Follow us on Twitter] '''''This wiki supports Linux Foundation ID single-sign-on and registration with the link at the top this page. 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Consult the [//meta.wikimedia.org/wiki/Help:Contents User's Guide] for information on using the wiki software. b9f82029c47078b7a04e2960d8e26e8a6054c1d5 1 2016-03-04T22:14:58Z MediaWiki default 0 wikitext text/x-wiki <strong>MediaWiki has been successfully installed.</strong> Consult the [//meta.wikimedia.org/wiki/Help:Contents User's Guide] for information on using the wiki software. == Getting started == * [//www.mediawiki.org/wiki/Manual:Configuration_settings Configuration settings list] * [//www.mediawiki.org/wiki/Manual:FAQ MediaWiki FAQ] * [https://lists.wikimedia.org/mailman/listinfo/mediawiki-announce MediaWiki release mailing list] * [//www.mediawiki.org/wiki/Localisation#Translation_resources Localise MediaWiki for your language] 48ae6e35148eb82b1c8ad85ab548f97ca752999a 0 7 15 2016-06-20T15:20:05Z Lukasstadler 8 Created page with "== Input == <pre> #include "R.h" </pre> == Result == <pre> #define Calloc(n, t) (t *) R_chk_calloc( (size_t) (n), sizeof(t) ) // Calloc used 5657 times in 240 packages #defin..." wikitext text/x-wiki == Input == <pre> #include "R.h" </pre> == Result == <pre> #define Calloc(n, t) (t *) R_chk_calloc( (size_t) (n), sizeof(t) ) // Calloc used 5657 times in 240 packages #define CallocCharBuf(n) (char *) R_chk_calloc((size_t) ((n)+1), sizeof(char)) // CallocCharBuf used 3 times in cplexAPI, patchDVI #define DOUBLE_DIGITS 53 // DOUBLE_DIGITS used 42 times in evd #define DOUBLE_EPS 2.2204460492503131e-16 // DOUBLE_EPS used 180 times in 40 packages #define DOUBLE_XMAX 1.7976931348623157e+308 // DOUBLE_XMAX used 63 times in 13 packages #define DOUBLE_XMIN 2.2250738585072014e-308 // DOUBLE_XMIN used 13 times in unmarked, deSolve, ifultools, spatstat #define ENABLE_NLS 1 // ENABLE_NLS used 80 times in 59 packages #define ERROR <defined> // ERROR used 6406 times in 293 packages #define F77_CALL(x) x_ // F77_CALL used 4269 times in 195 packages #define F77_COM(x) x_ // F77_COM used 2 times in igraph #define F77_COMDECL(x) x_ // F77_COMDECL used 2 times in igraph #define F77_NAME(x) x_ // F77_NAME used 1913 times in 117 packages #define F77_SUB(x) x_ // F77_SUB used 771 times in 89 packages #define Free(p) (R_chk_free( (void *)(p) ), (p) = __null) // Free used 21329 times in 683 packages #define HAVE_ALLOCA_H 1 // HAVE_ALLOCA_H used 15 times in treatSens, Matrix, TMB, pbdZMQ, ore, dbarts #define HAVE_AQUA 1 // HAVE_AQUA used 13 times in 11 packages #define HAVE_F77_UNDERSCORE 1 // HAVE_F77_UNDERSCORE used 2 times in igraph #define IEEE_754 1 // IEEE_754 used 47 times in igraph, Rcpp, data.table, stringi #define ISNA(x) R_IsNA(x) // ISNA used 649 times in 100 packages #define ISNAN(x) R_isnancpp(x) // ISNAN used 1342 times in 146 packages #define IndexWidth Rf_IndexWidth // IndexWidth unused #define LOCAL_EVALUATOR // LOCAL_EVALUATOR used 11 times in rggobi, XML, ifultools, RGtk2 #define LibExport // LibExport used 2 times in hsmm #define LibExtern extern // LibExtern used 4 times in rJava #define LibImport // LibImport unused #define MESSAGE <defined> // MESSAGE used 172 times in 33 packages #define M_1_PI 0.318309886183790671537767526745028724 // M_1_PI used 42 times in SpatialExtremes, decon, mvabund, geoR, geoRglm, ExomeDepth, libamtrack, miRada, RandomFields, DescTools #define M_2_PI 0.636619772367581343075535053490057448 // M_2_PI used 27 times in RandomFieldsUtils, dynaTree, ExomeDepth, RandomFields, svd, DescTools, spatstat #define M_2_SQRTPI 1.12837916709551257389615890312154517 // M_2_SQRTPI used 6 times in excursions, PearsonDS, SpecsVerification, ExomeDepth #define M_E 2.71828182845904523536028747135266250 // M_E used 40 times in Runuran, lamW, gmum.r, ExomeDepth, CEC, PoweR, TMB, Bmix, tgp, RcppShark #define M_LN10 2.30258509299404568401799145468436421 // M_LN10 used 27 times in monomvn, rphast, secr, Runuran, rtfbs, PlayerRatings, ExomeDepth, spaMM, logistf, laGP #define M_LN2 0.693147180559945309417232121458176568 // M_LN2 used 166 times in 30 packages #define M_LOG10E 0.434294481903251827651128918916605082 // M_LOG10E used 2 times in ExomeDepth #define M_LOG2E 1.44269504088896340735992468100189214 // M_LOG2E used 2 times in ExomeDepth #define M_PI 3.14159265358979323846264338327950288 // M_PI used 1853 times in 207 packages #define M_PI_2 1.57079632679489661923132169163975144 // M_PI_2 used 149 times in 28 packages #define M_PI_4 0.785398163397448309615660845819875721 // M_PI_4 used 18 times in 12 packages #define M_SQRT1_2 0.707106781186547524400844362104849039 // M_SQRT1_2 used 26 times in SpatialExtremes, gmwm, excursions, forecast, subrank, dplR, ExomeDepth, SpecsVerification #define M_SQRT2 1.41421356237309504880168872420969808 // M_SQRT2 used 72 times in 23 packages #define Memcpy(p,q,n) memcpy( p, q, (size_t)(n) * sizeof(*p) ) // Memcpy used 483 times in 32 packages #define Memzero(p,n) memset(p, 0, (size_t)(n) * sizeof(*p)) // Memzero used 5 times in Matrix #define NA_INTEGER R_NaInt // NA_INTEGER used 1520 times in 183 packages #define NA_LOGICAL R_NaInt // NA_LOGICAL used 355 times in 73 packages #define NA_REAL R_NaReal // NA_REAL used 1667 times in 226 packages #define NORET __attribute__((noreturn)) // NORET unused #define NULL_ENTRY // NULL_ENTRY used 170 times in 12 packages #define PI 3.14159265358979323846264338327950288 // PI unused #define PROBLEM <defined> // PROBLEM used 861 times in 78 packages #define RECOVER <defined> // RECOVER used 170 times in 14 packages #define R_ARITH_H_ // R_ARITH_H_ unused #define R_COMPLEX_H // R_COMPLEX_H used 1 times in uniqueAtomMat #define R_Calloc(n, t) (t *) R_chk_calloc( (size_t) (n), sizeof(t) ) // R_Calloc used 81 times in clpAPI, cplexAPI, poppr, rLindo, glpkAPI #define R_ERROR_H_ // R_ERROR_H_ unused #define R_EXT_BOOLEAN_H_ // R_EXT_BOOLEAN_H_ used 2 times in jpeg, Rcpp11 #define R_EXT_CONSTANTS_H_ // R_EXT_CONSTANTS_H_ unused #define R_EXT_MEMORY_H_ // R_EXT_MEMORY_H_ unused #define R_EXT_PRINT_H_ // R_EXT_PRINT_H_ used 6 times in spTDyn, spTimer #define R_EXT_UTILS_H_ // R_EXT_UTILS_H_ unused #define R_FINITE(x) R_finite(x) // R_FINITE used 1387 times in 145 packages #define R_Free(p) (R_chk_free( (void *)(p) ), (p) = __null) // R_Free used 78 times in clpAPI, cplexAPI, poppr, glpkAPI #define R_INLINE inline // R_INLINE used 330 times in 34 packages #define R_PROBLEM_BUFSIZE 4096 // R_PROBLEM_BUFSIZE unused #define R_RANDOM_H // R_RANDOM_H unused #define R_RCONFIG_H // R_RCONFIG_H unused #define R_RS_H // R_RS_H unused #define R_R_H // R_R_H used 9 times in TMB, uniqueAtomMat, DatABEL, GenABEL, VariABEL #define R_Realloc(p,n,t) (t *) R_chk_realloc( (void *)(p), (size_t)((n) * sizeof(t)) ) // R_Realloc used 3 times in poppr, seqminer, gpuR #define Realloc(p,n,t) (t *) R_chk_realloc( (void *)(p), (size_t)((n) * sizeof(t)) ) // Realloc used 244 times in 57 packages #define SINGLE_BASE 2 // SINGLE_BASE unused #define SINGLE_EPS 1.19209290e-7F // SINGLE_EPS unused #define SINGLE_XMAX 3.40282347e+38F // SINGLE_XMAX used 4 times in mapproj #define SINGLE_XMIN 1.17549435e-38F // SINGLE_XMIN unused #define SINT_MAX 2147483647 // SINT_MAX used 4 times in robust, AnalyzeFMRI #define SINT_MIN (-2147483647 -1) // SINT_MIN used 2 times in robust #define SIZEOF_SIZE_T 8 // SIZEOF_SIZE_T used 1 times in PythonInR #define SUPPORT_MBCS 1 // SUPPORT_MBCS used 1 times in bibtex #define SUPPORT_UTF8 1 // SUPPORT_UTF8 used 3 times in tau, rindex, stringi #define StringFalse Rf_StringFalse // StringFalse used 3 times in iotools #define StringTrue Rf_StringTrue // StringTrue used 3 times in iotools #define USING_R // USING_R used 238 times in 29 packages #define WARN <defined> // WARN used 122 times in 20 packages #define WARNING <defined> // WARNING used 957 times in 190 packages #define cPsort Rf_cPsort // cPsort unused #define error Rf_error // error used 63771 times in 1109 packages #define iPsort Rf_iPsort // iPsort used 3 times in matrixStats, robustbase #define isBlankString Rf_isBlankString // isBlankString used 1 times in iotools #define rPsort Rf_rPsort // rPsort used 63 times in 15 packages #define revsort Rf_revsort // revsort used 60 times in 20 packages #define setIVector Rf_setIVector // setIVector unused #define setRVector Rf_setRVector // setRVector used 3 times in RcppClassic, RcppClassicExamples #define warning Rf_warning // warning used 7679 times in 434 packages # /Users/ls/Source/git/fastr/com.oracle.truffle.r.native/include/R.h typedef double Sfloat; // Sfloat used 440 times in AnalyzeFMRI, wavethresh, IGM.MEA, spatial, LS2W, robust, MASS, PBSmapping typedef int Sint; // Sint used 2750 times in 48 packages extern "C" { void R_FlushConsole(void); // R_FlushConsole used 651 times in 78 packages void R_ProcessEvents(void); // R_ProcessEvents used 275 times in 39 packages } # /Users/ls/Source/git/fastr/com.oracle.truffle.r.native/include/R_ext/Arith.h extern "C" { extern double R_NaN; // R_NaN used 469 times in 68 packages extern double R_PosInf; // R_PosInf used 562 times in 112 packages extern double R_NegInf; // R_NegInf used 699 times in 105 packages extern double R_NaReal; // R_NaReal used 140 times in 34 packages // NA_REAL used 1667 times in 226 packages extern int R_NaInt; // R_NaInt used 58 times in 20 packages // NA_INTEGER used 1520 times in 183 packages // NA_LOGICAL used 355 times in 73 packages int R_IsNA(double); // R_IsNA used 161 times in 40 packages int R_IsNaN(double); // R_IsNaN used 75 times in 28 packages int R_finite(double); // R_finite used 232 times in 44 packages int R_isnancpp(double); // R_isnancpp used 8 times in igraph, PwrGSD } # /Users/ls/Source/git/fastr/com.oracle.truffle.r.native/include/R_ext/Boolean.h extern "C" { typedef enum { FALSE = 0, TRUE } Rboolean; } # /Users/ls/Source/git/fastr/com.oracle.truffle.r.native/include/R_ext/Complex.h extern "C" { typedef struct { double r; double i; } Rcomplex; // Rcomplex used 893 times in 47 packages } # /Users/ls/Source/git/fastr/com.oracle.truffle.r.native/include/R_ext/Error.h extern "C" { void __attribute__((noreturn)) Rf_error(const char *, ...); void __attribute__((noreturn)) UNIMPLEMENTED(const char *); void __attribute__((noreturn)) WrongArgCount(const char *); void Rf_warning(const char *, ...); // Rf_warning used 316 times in 66 packages // warning used 7679 times in 434 packages void R_ShowMessage(const char *s); // R_ShowMessage used 104 times in Rserve, rJava, HiPLARM } # /Users/ls/Source/git/fastr/com.oracle.truffle.r.native/include/R_ext/Memory.h extern "C" { void* vmaxget(void); // vmaxget used 279 times in 20 packages void vmaxset(const void *); // vmaxset used 279 times in 20 packages void R_gc(void); // R_gc used 6 times in TMB, excel.link, gmatrix, microbenchmark int R_gc_running(); // R_gc_running unused char* R_alloc(size_t, int); // R_alloc used 7787 times in 330 packages long double *R_allocLD(size_t nelem); char* S_alloc(long, int); // S_alloc used 540 times in 50 packages char* S_realloc(char *, long, long, int); // S_realloc used 55 times in 11 packages } # /Users/ls/Source/git/fastr/com.oracle.truffle.r.native/include/R_ext/Print.h extern "C" { void Rprintf(const char *, ...); // Rprintf used 33813 times in 729 packages void REprintf(const char *, ...); // REprintf used 2531 times in 135 packages } # /Users/ls/Source/git/fastr/com.oracle.truffle.r.native/include/R_ext/RS.h extern "C" { extern void *R_chk_calloc(size_t, size_t); // R_chk_calloc used 6 times in rpart, XML, itree, ifultools, mgcv extern void *R_chk_realloc(void *, size_t); // R_chk_realloc used 5 times in seqminer, gpuR, ifultools, mgcv extern void R_chk_free(void *); // R_chk_free used 2 times in mgcv void call_R(char*, long, void**, char**, long*, char**, long, char**); // call_R used 2 times in PoweR } # /Users/ls/Source/git/fastr/com.oracle.truffle.r.native/include/R_ext/Random.h extern "C" { typedef enum { WICHMANN_HILL, MARSAGLIA_MULTICARRY, SUPER_DUPER, MERSENNE_TWISTER, KNUTH_TAOCP, USER_UNIF, KNUTH_TAOCP2, LECUYER_CMRG } RNGtype; // RNGtype unused typedef enum { BUGGY_KINDERMAN_RAMAGE, AHRENS_DIETER, BOX_MULLER, USER_NORM, INVERSION, KINDERMAN_RAMAGE } N01type; // N01type unused void GetRNGstate(void); // GetRNGstate used 1753 times in 434 packages void PutRNGstate(void); // PutRNGstate used 1794 times in 427 packages double unif_rand(void); // unif_rand used 2135 times in 327 packages double norm_rand(void); // norm_rand used 408 times in 93 packages double exp_rand(void); // exp_rand used 110 times in 25 packages typedef unsigned int Int32; double * user_unif_rand(void); // user_unif_rand used 10 times in randaes, rstream, rngwell19937, SuppDists, randtoolbox, rlecuyer, Rrdrand void user_unif_init(Int32); // user_unif_init used 5 times in randaes, SuppDists, randtoolbox, rngwell19937 int * user_unif_nseed(void); // user_unif_nseed used 4 times in randaes, SuppDists, rngwell19937 int * user_unif_seedloc(void); // user_unif_seedloc used 4 times in randaes, SuppDists, rngwell19937 double * user_norm_rand(void); // user_norm_rand used 1 times in RcppZiggurat } # /Users/ls/Source/git/fastr/com.oracle.truffle.r.native/include/R_ext/Utils.h extern "C" { void R_isort(int*, int); // R_isort used 45 times in 18 packages void R_rsort(double*, int); // R_rsort used 210 times in 29 packages void R_csort(Rcomplex*, int); // R_csort unused void rsort_with_index(double *, int *, int); // rsort_with_index used 77 times in 40 packages void Rf_revsort(double*, int*, int); // Rf_revsort unused // revsort used 60 times in 20 packages void Rf_iPsort(int*, int, int); // Rf_iPsort unused // iPsort used 3 times in matrixStats, robustbase void Rf_rPsort(double*, int, int); // Rf_rPsort unused // rPsort used 63 times in 15 packages void Rf_cPsort(Rcomplex*, int, int); // Rf_cPsort unused // cPsort unused void R_qsort (double *v, size_t i, size_t j); // R_qsort used 10 times in extWeibQuant, pomp, robustbase, dplR, tclust, pcaPP void R_qsort_I (double *v, int *II, int i, int j); // R_qsort_I used 33 times in 15 packages void R_qsort_int (int *iv, size_t i, size_t j); // R_qsort_int unused void R_qsort_int_I(int *iv, int *II, int i, int j); // R_qsort_int_I used 19 times in ff, matrixStats, arules, Rborist, slam, eco, bnlearn const char *R_ExpandFileName(const char *); // R_ExpandFileName used 42 times in 20 packages void Rf_setIVector(int*, int, int); // Rf_setIVector unused // setIVector unused void Rf_setRVector(double*, int, double); // Rf_setRVector unused // setRVector used 3 times in RcppClassic, RcppClassicExamples Rboolean Rf_StringFalse(const char *); // Rf_StringFalse unused // StringFalse used 3 times in iotools Rboolean Rf_StringTrue(const char *); // Rf_StringTrue unused // StringTrue used 3 times in iotools Rboolean Rf_isBlankString(const char *); // Rf_isBlankString unused // isBlankString used 1 times in iotools double R_atof(const char *str); // R_atof used 9 times in SSN, tree, foreign, iotools double R_strtod(const char *c, char **end); // R_strtod used 4 times in ape, iotools char *R_tmpnam(const char *prefix, const char *tempdir); // R_tmpnam used 2 times in geometry char *R_tmpnam2(const char *prefix, const char *tempdir, const char *fileext); // R_tmpnam2 unused void R_CheckUserInterrupt(void); // R_CheckUserInterrupt used 1487 times in 234 packages void R_CheckStack(void); // R_CheckStack used 115 times in vcrpart, actuar, cplm, lme4, Matrix, GNE, randtoolbox, HiPLARM, rngWELL, pedigreemm void R_CheckStack2(size_t); // R_CheckStack2 unused int findInterval(double *xt, int n, double x, // findInterval used 11 times in BSquare, DNAprofiles, unfoldr, chebpol, pomp, eco, protViz, PBSmapping, spatstat Rboolean rightmost_closed, Rboolean all_inside, int ilo, int *mflag); void find_interv_vec(double *xt, int *n, double *x, int *nx, // find_interv_vec unused int *rightmost_closed, int *all_inside, int *indx); void R_max_col(double *matrix, int *nr, int *nc, int *maxes, int *ties_meth); // R_max_col used 2 times in geostatsp, MNP } </pre> == Stats == <pre> 0 1 2 3 4 5 6 7 8 9 10+ Macro: 15 3 6 3 2 1 2 0 0 1 36 (usage count) (69) 15 11 4 2 5 2 1 1 1 0 27 (distinct package count) Function: 8 2 4 4 3 2 2 0 1 1 32 (usage count) (59) 8 7 5 3 4 1 1 2 0 1 27 (distinct package count) Variable: 0 0 0 0 0 0 0 0 0 0 5 (usage count) (5) 0 0 0 0 0 0 0 0 0 0 5 (distinct package count) TypeDef: 2 0 0 0 0 0 0 0 0 0 3 (usage count) (5) 2 0 0 0 0 0 0 0 1 0 2 (distinct package count) Alias: 3 1 2 4 1 0 0 0 0 0 11 (usage count) (22) 3 6 2 0 0 0 0 0 0 0 11 (distinct package count) </pre> (e.g., 5 functions were referenced by exactly 2 distinct packages, and 3 typedefs have 10 or more individual usages) ed4b49a2a79a470fad9a630430b0c950967084cd 0 9 17 2016-06-20T15:20:13Z Lukasstadler 8 Created page with "== Input == <pre> #define USE_RINTERNALS #include "Rinternals.h" </pre> == Result == <pre> #define ANYSXP 18 // ANYSXP used 14 time..." wikitext text/x-wiki == Input == <pre> #define USE_RINTERNALS #include "Rinternals.h" </pre> == Result == <pre> #define ANYSXP 18 // ANYSXP used 14 times in RPostgreSQL, Rcpp11, seqminer, Rcpp, pryr, rtkpp, rtkore, RGtk2 #define ATTRIB(x) ((x)->attrib) // ATTRIB used 83 times in 20 packages #define BCODESXP 21 // BCODESXP used 15 times in rcppbugs, Rcpp11, seqminer, Rcpp, pryr, rtkpp, rtkore #define BCODE_CODE(x) ((x)->u.listsxp.carval) // BCODE_CODE unused #define BCODE_CONSTS(x) ((x)->u.listsxp.cdrval) // BCODE_CONSTS unused #define BCODE_EXPR(x) ((x)->u.listsxp.tagval) // BCODE_EXPR unused #define BODY(x) ((x)->u.closxp.body) // BODY used 48 times in 15 packages #define BODY_EXPR(e) R_ClosureExpr(e) // BODY_EXPR unused #define BUILTINSXP 8 // BUILTINSXP used 24 times in 11 packages #define CAAR(e) ((((e)->u.listsxp.carval))->u.listsxp.carval) // CAAR unused #define CAD4R(e) ((((((((((e)->u.listsxp.cdrval))->u.listsxp.cdrval))->u.listsxp.cdrval))->u.listsxp.cdrval))->u.listsxp.carval) // CAD4R used 14 times in earth, foreign, actuar #define CADDDR(e) ((((((((e)->u.listsxp.cdrval))->u.listsxp.cdrval))->u.listsxp.cdrval))->u.listsxp.carval) // CADDDR used 21 times in RPostgreSQL, foreign, actuar, bibtex #define CADDR(e) ((((((e)->u.listsxp.cdrval))->u.listsxp.cdrval))->u.listsxp.carval) // CADDR used 52 times in 11 packages #define CADR(e) ((((e)->u.listsxp.cdrval))->u.listsxp.carval) // CADR used 104 times in 17 packages #define CAR(e) ((e)->u.listsxp.carval) // CAR used 575 times in 63 packages #define CDAR(e) ((((e)->u.listsxp.carval))->u.listsxp.cdrval) // CDAR unused #define CDDDR(e) ((((((e)->u.listsxp.cdrval))->u.listsxp.cdrval))->u.listsxp.cdrval) // CDDDR unused #define CDDR(e) ((((e)->u.listsxp.cdrval))->u.listsxp.cdrval) // CDDR used 52 times in Rlabkey, Rcpp11, dplyr, proxy, Rcpp, slam, tikzDevice, OpenCL, svd #define CDR(e) ((e)->u.listsxp.cdrval) // CDR used 4523 times in 76 packages #define CHAR(x) ((const char *) (((SEXPREC_ALIGN *) (x)) + 1)) // CHAR used 4405 times in 362 packages #define CHARSXP 9 // CHARSXP used 106 times in 33 packages #define CLOENV(x) ((x)->u.closxp.env) // CLOENV used 23 times in Rcpp11, covr, pomp, Rcpp, pryr, testthat, qtbase #define CLOSXP 3 // CLOSXP used 83 times in 30 packages #define COMPLEX(x) ((Rcomplex *) (((SEXPREC_ALIGN *) (x)) + 1)) // COMPLEX used 1697 times in 71 packages #define CONS(a, b) Rf_cons((a), (b)) // CONS used 458 times in 30 packages #define CPLXSXP 15 // CPLXSXP used 409 times in 49 packages #define CreateTag Rf_CreateTag // CreateTag used 1 times in rgp #define DATAPTR(x) (((SEXPREC_ALIGN *) (x)) + 1) // DATAPTR used 113 times in 11 packages #define DDVAL(x) ((x)->sxpinfo.gp & 1) // DDVAL unused #define DDVAL_MASK 1 // DDVAL_MASK unused #define DECREMENT_REFCNT(x) do {} while(0) // DECREMENT_REFCNT unused #define DISABLE_REFCNT(x) do {} while(0) // DISABLE_REFCNT unused #define DOTSXP 17 // DOTSXP used 16 times in RPostgreSQL, PythonInR, Rcpp11, seqminer, Rcpp, pryr, rtkpp, spikeSlabGAM, rtkore #define DropDims Rf_DropDims // DropDims unused #define ENABLE_NLS 1 // ENABLE_NLS used 80 times in 59 packages #define ENABLE_REFCNT(x) do {} while(0) // ENABLE_REFCNT unused #define ENCLOS(x) ((x)->u.envsxp.enclos) // ENCLOS used 7 times in Rcpp, pryr, rJava, Rcpp11, RGtk2 #define ENVFLAGS(x) ((x)->sxpinfo.gp) // ENVFLAGS unused #define ENVSXP 4 // ENVSXP used 63 times in 25 packages #define EXPRSXP 20 // EXPRSXP used 84 times in 14 packages #define EXTPTRSXP 22 // EXTPTRSXP used 386 times in 55 packages #define EXTPTR_PROT(x) ((x)->u.listsxp.cdrval) // EXTPTR_PROT used 5 times in rJava, pryr #define EXTPTR_PTR(x) ((x)->u.listsxp.carval) // EXTPTR_PTR used 428 times in 15 packages #define EXTPTR_TAG(x) ((x)->u.listsxp.tagval) // EXTPTR_TAG used 9 times in excel.link, pryr, rJava, gsl #define FORMALS(x) ((x)->u.closxp.formals) // FORMALS used 15 times in qtpaint, RSclient, PBSddesolve, Rserve, covr, pryr, rgp, testthat, RandomFields #define FRAME(x) ((x)->u.envsxp.frame) // FRAME used 19 times in deTestSet, IRISSeismic, pryr, BayesBridge, datamap, BayesLogit #define FREESXP 31 // FREESXP used 4 times in rtkpp, rtkore #define FUNSXP 99 // FUNSXP used 6 times in dplyr, rtkpp, data.table, rtkore #define GetArrayDimnames Rf_GetArrayDimnames // GetArrayDimnames unused #define GetColNames Rf_GetColNames // GetColNames unused #define GetMatrixDimnames Rf_GetMatrixDimnames // GetMatrixDimnames used 2 times in Kmisc, optmatch #define GetOption Rf_GetOption // GetOption used 5 times in rgl, gmp, Cairo, RGtk2 #define GetOption1 Rf_GetOption1 // GetOption1 used 1 times in PCICt #define GetOptionDigits Rf_GetOptionDigits // GetOptionDigits unused #define GetOptionWidth Rf_GetOptionWidth // GetOptionWidth unused #define GetRowNames Rf_GetRowNames // GetRowNames unused #define HASHTAB(x) ((x)->u.envsxp.hashtab) // HASHTAB used 12 times in Rcpp, pryr, datamap, Rcpp11, qtbase #define HAVE_ALLOCA_H 1 // HAVE_ALLOCA_H used 15 times in treatSens, Matrix, TMB, pbdZMQ, ore, dbarts #define HAVE_AQUA 1 // HAVE_AQUA used 13 times in 11 packages #define HAVE_F77_UNDERSCORE 1 // HAVE_F77_UNDERSCORE used 2 times in igraph #define IEEE_754 1 // IEEE_754 used 47 times in igraph, Rcpp, data.table, stringi #define INCREMENT_NAMED(x) do { SEXP __x__ = (x); if (((__x__)->sxpinfo.named) != 2) (((__x__)->sxpinfo.named)=(((__x__)->sxpinfo.named) + 1)); } while (0) // INCREMENT_NAMED unused #define INCREMENT_REFCNT(x) do {} while(0) // INCREMENT_REFCNT unused #define INLINE_PROTECT // INLINE_PROTECT unused #define INTEGER(x) ((int *) (((SEXPREC_ALIGN *) (x)) + 1)) // INTEGER used 41659 times in 758 packages #define INTERNAL(x) ((x)->u.symsxp.internal) // INTERNAL used 1014 times in 63 packages #define INTSXP 13 // INTSXP used 6341 times in 471 packages #define ISNA(x) R_IsNA(x) // ISNA used 649 times in 100 packages #define ISNAN(x) R_isnancpp(x) // ISNAN used 1342 times in 146 packages #define IS_GETTER_CALL(call) (((((call)->u.listsxp.cdrval))->u.listsxp.carval) == R_TmpvalSymbol) // IS_GETTER_CALL unused #define IS_LONG_VEC(x) ((((VECSEXP) (x))->vecsxp.length) == -1) // IS_LONG_VEC used 1 times in RProtoBuf #define IS_S4_OBJECT(x) ((x)->sxpinfo.gp & ((unsigned short)(1<<4))) // IS_S4_OBJECT used 23 times in Rmosek, Runuran, data.table, xts, Matrix, slam, zoo, HiPLARM, OpenMx, tau #define IS_SCALAR(x, type) (((x)->sxpinfo.type) == (type) && (((VECSEXP) (x))->vecsxp.length) == 1) // IS_SCALAR unused #define IS_SIMPLE_SCALAR(x, type) ((((x)->sxpinfo.type) == (type) && (((VECSEXP) (x))->vecsxp.length) == 1) && ((x)->attrib) == R_NilValue) // IS_SIMPLE_SCALAR unused #define IndexWidth Rf_IndexWidth // IndexWidth unused #define LANGSXP 6 // LANGSXP used 1276 times in 53 packages #define LCONS(a, b) Rf_lcons((a), (b)) // LCONS used 212 times in 24 packages #define LENGTH(x) (((((VECSEXP) (x))->vecsxp.length) == -1) ? R_BadLongVector(x, \"/var/folders/t8/1ry582nx6438y8pn6gk20f3c0000gn/T/preprocessor_test2233054488227688894.cpp\", 453) : (((VECSEXP) (x))->vecsxp.length)) // LENGTH used 5845 times in 356 packages #define LEVELS(x) ((x)->sxpinfo.gp) // LEVELS used 18 times in rtdists, rPref, BsMD, data.table, stringi, dplyr, OBsMD, pbdZMQ, astrochron, RandomFields #define LGLSXP 10 // LGLSXP used 1430 times in 166 packages #define LISTSXP 2 // LISTSXP used 87 times in 21 packages #define LISTVAL(x) ((x)->u.listsxp) // LISTVAL unused #define LOGICAL(x) ((int *) (((SEXPREC_ALIGN *) (x)) + 1)) // LOGICAL used 4473 times in 288 packages #define LONG_VECTOR_SUPPORT // LONG_VECTOR_SUPPORT used 56 times in stringdist, matrixStats, RApiSerialize, Rhpc, pbdMPI, Rcpp11, Matrix #define LONG_VEC_LENGTH(x) ((R_long_vec_hdr_t *) (x))[-1].lv_length // LONG_VEC_LENGTH used 1 times in Rcpp11 #define LONG_VEC_TRUELENGTH(x) ((R_long_vec_hdr_t *) (x))[-1].lv_truelength // LONG_VEC_TRUELENGTH unused #define LibExport // LibExport used 2 times in hsmm #define LibExtern extern // LibExtern used 4 times in rJava #define LibImport // LibImport unused #define MARK(x) ((x)->sxpinfo.mark) // MARK used 251 times in 21 packages #define MARK_NOT_MUTABLE(x) (((x)->sxpinfo.named)=(2)) // MARK_NOT_MUTABLE unused #define MAX_NUM_SEXPTYPE (1<<5) // MAX_NUM_SEXPTYPE unused #define MAYBE_REFERENCED(x) (! (((x)->sxpinfo.named) == 0)) // MAYBE_REFERENCED unused #define MAYBE_SHARED(x) (((x)->sxpinfo.named) > 1) // MAYBE_SHARED unused #define MISSING(x) ((x)->sxpinfo.gp & 15) // MISSING used 125 times in 25 packages #define MISSING_MASK 15 // MISSING_MASK used 10 times in rJPSGCS #define NAMED(x) ((x)->sxpinfo.named) // NAMED used 62 times in 22 packages #define NAMEDMAX 2 // NAMEDMAX unused #define NA_INTEGER R_NaInt // NA_INTEGER used 1520 times in 183 packages #define NA_LOGICAL R_NaInt // NA_LOGICAL used 355 times in 73 packages #define NA_REAL R_NaReal // NA_REAL used 1667 times in 226 packages #define NA_STRING R_NaString // NA_STRING used 574 times in 90 packages #define NEWSXP 30 // NEWSXP used 4 times in rtkpp, rtkore #define NILSXP 0 // NILSXP used 169 times in 44 packages #define NORET __attribute__((noreturn)) // NORET unused #define NOT_SHARED(x) (! (((x)->sxpinfo.named) > 1)) // NOT_SHARED unused #define NO_REFERENCES(x) (((x)->sxpinfo.named) == 0) // NO_REFERENCES unused #define NonNullStringMatch Rf_NonNullStringMatch // NonNullStringMatch used 8 times in proxy, arules, arulesSequences, cba #define OBJECT(x) ((x)->sxpinfo.obj) // OBJECT used 102 times in 28 packages #define PREXPR(e) R_PromiseExpr(e) // PREXPR used 4 times in igraph, lazyeval #define PRINTNAME(x) ((x)->u.symsxp.pname) // PRINTNAME used 92 times in 29 packages #define PROMSXP 5 // PROMSXP used 43 times in 14 packages #define PROTECT(s) Rf_protect(s) // PROTECT used 24686 times in 767 packages #define PROTECT_WITH_INDEX(x,i) R_ProtectWithIndex(x,i) // PROTECT_WITH_INDEX used 91 times in 27 packages #define PairToVectorList Rf_PairToVectorList // PairToVectorList used 7 times in cba, rcdd #define PrintValue Rf_PrintValue // PrintValue used 119 times in 13 packages #define RAW(x) ((Rbyte *) (((SEXPREC_ALIGN *) (x)) + 1)) // RAW used 880 times in 99 packages #define RAWSXP 24 // RAWSXP used 587 times in 92 packages #define RDEBUG(x) ((x)->sxpinfo.debug) // RDEBUG used 69 times in rmetasim #define REAL(x) ((double *) (((SEXPREC_ALIGN *) (x)) + 1)) // REAL used 30947 times in 687 packages #define REALSXP 14 // REALSXP used 10171 times in 573 packages #define REFCNT(x) 0 // REFCNT unused #define REFCNTMAX (4 - 1) // REFCNTMAX unused #define REPROTECT(x,i) R_Reprotect(x,i) // REPROTECT used 130 times in 25 packages #define RSTEP(x) ((x)->sxpinfo.spare) // RSTEP unused #define RTRACE(x) ((x)->sxpinfo.trace) // RTRACE unused #define R_ALLOCATOR_TYPE // R_ALLOCATOR_TYPE unused #define R_ARITH_H_ // R_ARITH_H_ unused #define R_COMPLEX_H // R_COMPLEX_H used 1 times in uniqueAtomMat #define R_CheckStack() do { void __attribute__((noreturn)) R_SignalCStackOverflow(intptr_t); int dummy; intptr_t usage = R_CStackDir * (R_CStackStart - (uintptr_t)&dummy); if(R_CStackLimit != -1 && usage > ((intptr_t) R_CStackLimit)) R_SignalCStackOverflow(usage); } while (FALSE) // R_CheckStack used 115 times in vcrpart, actuar, cplm, lme4, Matrix, GNE, randtoolbox, HiPLARM, rngWELL, pedigreemm #define R_ERROR_H_ // R_ERROR_H_ unused #define R_EXT_BOOLEAN_H_ // R_EXT_BOOLEAN_H_ used 2 times in jpeg, Rcpp11 #define R_EXT_MEMORY_H_ // R_EXT_MEMORY_H_ unused #define R_EXT_PRINT_H_ // R_EXT_PRINT_H_ used 6 times in spTDyn, spTimer #define R_EXT_UTILS_H_ // R_EXT_UTILS_H_ unused #define R_FINITE(x) R_finite(x) // R_FINITE used 1387 times in 145 packages #define R_INLINE inline // R_INLINE used 330 times in 34 packages #define R_INTERNALS_H_ // R_INTERNALS_H_ used 7 times in uniqueAtomMat, rtkpp, rtkore, spatstat #define R_LEN_T_MAX 2147483647 // R_LEN_T_MAX used 4 times in stringdist, matrixStats, FREGAT, Rcpp11 #define R_LONG_VEC_TOKEN -1 // R_LONG_VEC_TOKEN used 1 times in Rcpp11 #define R_RCONFIG_H // R_RCONFIG_H unused #define R_SHORT_LEN_MAX 2147483647 // R_SHORT_LEN_MAX used 1 times in pbdMPI #define R_XDR_DOUBLE_SIZE 8 // R_XDR_DOUBLE_SIZE used 2 times in rgdal #define R_XDR_INTEGER_SIZE 4 // R_XDR_INTEGER_SIZE used 3 times in rgdal #define R_XLEN_T_MAX 4503599627370496 // R_XLEN_T_MAX used 7 times in stringdist, Matrix, matrixStats, RApiSerialize, Rhpc #define S3Class Rf_S3Class // S3Class used 4 times in RInside, littler #define S4SXP 25 // S4SXP used 71 times in 15 packages #define S4_OBJECT_MASK ((unsigned short)(1<<4)) // S4_OBJECT_MASK unused #define SETLENGTH(x,v) do { SEXP sl__x__ = (x); R_xlen_t sl__v__ = (v); if (((((VECSEXP) (sl__x__))->vecsxp.length) == -1)) (((R_long_vec_hdr_t *) (sl__x__))[-1].lv_length = (sl__v__)); else ((((VECSEXP) (sl__x__))->vecsxp.length) = ((R_len_t) sl__v__)); } while (0) // SETLENGTH used 65 times in 11 packages #define SETLEVELS(x,v) (((x)->sxpinfo.gp)=((unsigned short)v)) // SETLEVELS used 2 times in Rcpp11 #define SET_DDVAL(x,v) ((v) ? (((x)->sxpinfo.gp) |= 1) : (((x)->sxpinfo.gp) &= ~1)) // SET_DDVAL unused #define SET_DDVAL_BIT(x) (((x)->sxpinfo.gp) |= 1) // SET_DDVAL_BIT unused #define SET_ENVFLAGS(x,v) (((x)->sxpinfo.gp)=(v)) // SET_ENVFLAGS unused #define SET_LONG_VEC_LENGTH(x,v) (((R_long_vec_hdr_t *) (x))[-1].lv_length = (v)) // SET_LONG_VEC_LENGTH unused #define SET_LONG_VEC_TRUELENGTH(x,v) (((R_long_vec_hdr_t *) (x))[-1].lv_truelength = (v)) // SET_LONG_VEC_TRUELENGTH unused #define SET_MISSING(x,v) do { SEXP __x__ = (x); int __v__ = (v); int __other_flags__ = __x__->sxpinfo.gp & ~15; __x__->sxpinfo.gp = __other_flags__ | __v__; } while (0) // SET_MISSING used 1 times in sprint #define SET_NAMED(x, v) (((x)->sxpinfo.named)=(v)) // SET_NAMED used 10 times in dplyr, yaml, data.table, iotools, RSQLite #define SET_OBJECT(x,v) (((x)->sxpinfo.obj)=(v)) // SET_OBJECT used 32 times in RSclient, reshape2, Rserve, data.table, actuar, dplyr, proxy, rmongodb, slam, tau #define SET_RDEBUG(x,v) (((x)->sxpinfo.debug)=(v)) // SET_RDEBUG unused #define SET_REFCNT(x,v) do {} while(0) // SET_REFCNT unused #define SET_RSTEP(x,v) (((x)->sxpinfo.spare)=(v)) // SET_RSTEP unused #define SET_RTRACE(x,v) (((x)->sxpinfo.trace)=(v)) // SET_RTRACE unused #define SET_S4_OBJECT(x) (((x)->sxpinfo.gp) |= ((unsigned short)(1<<4))) // SET_S4_OBJECT used 12 times in RSclient, redland, Rserve, data.table, FREGAT, rJPSGCS, tau #define SET_SHORT_VEC_LENGTH SET_SHORT_VEC_LENGTH // SET_SHORT_VEC_LENGTH unused #define SET_SHORT_VEC_TRUELENGTH SET_SHORT_VEC_TRUELENGTH // SET_SHORT_VEC_TRUELENGTH unused #define SET_TRACKREFS(x,v) do {} while(0) // SET_TRACKREFS unused #define SET_TRUELENGTH(x,v) do { SEXP sl__x__ = (x); R_xlen_t sl__v__ = (v); if (((((VECSEXP) (sl__x__))->vecsxp.length) == -1)) (((R_long_vec_hdr_t *) (sl__x__))[-1].lv_truelength = (sl__v__)); else ((((VECSEXP) (sl__x__))->vecsxp.truelength) = ((R_len_t) sl__v__)); } while (0) // SET_TRUELENGTH used 26 times in data.table #define SET_TYPEOF(x,v) (((x)->sxpinfo.type)=(v)) // SET_TYPEOF used 38 times in 21 packages #define SEXPREC_HEADER <defined> // SEXPREC_HEADER unused #define SHORT_VEC_LENGTH(x) (((VECSEXP) (x))->vecsxp.length) // SHORT_VEC_LENGTH used 1 times in Rcpp11 #define SHORT_VEC_TRUELENGTH(x) (((VECSEXP) (x))->vecsxp.truelength) // SHORT_VEC_TRUELENGTH unused #define SIZEOF_SIZE_T 8 // SIZEOF_SIZE_T used 1 times in PythonInR #define SPECIALSXP 7 // SPECIALSXP used 22 times in RPostgreSQL, PythonInR, Rcpp11, purrr, seqminer, Rcpp, yaml, pryr, rtkpp, rtkore #define STRING_ELT(x,i) ((SEXP *) (((SEXPREC_ALIGN *) (x)) + 1))[i] // STRING_ELT used 4143 times in 333 packages #define STRING_PTR(x) ((SEXP *) (((SEXPREC_ALIGN *) (x)) + 1)) // STRING_PTR used 65 times in 14 packages #define STRSXP 16 // STRSXP used 3247 times in 327 packages #define SUPPORT_MBCS 1 // SUPPORT_MBCS used 1 times in bibtex #define SUPPORT_UTF8 1 // SUPPORT_UTF8 used 3 times in tau, rindex, stringi #define SYMSXP 1 // SYMSXP used 94 times in 25 packages #define SYMVALUE(x) ((x)->u.symsxp.value) // SYMVALUE unused #define ScalarComplex Rf_ScalarComplex // ScalarComplex unused #define ScalarInteger Rf_ScalarInteger // ScalarInteger used 704 times in 88 packages #define ScalarLogical Rf_ScalarLogical // ScalarLogical used 450 times in 64 packages #define ScalarRaw Rf_ScalarRaw // ScalarRaw used 4 times in qtbase, RGtk2 #define ScalarReal Rf_ScalarReal // ScalarReal used 330 times in 65 packages #define ScalarString Rf_ScalarString // ScalarString used 198 times in 37 packages #define StringBlank Rf_StringBlank // StringBlank unused #define StringFalse Rf_StringFalse // StringFalse used 3 times in iotools #define StringTrue Rf_StringTrue // StringTrue used 3 times in iotools #define TAG(e) ((e)->u.listsxp.tagval) // TAG used 513 times in 40 packages #define TRACKREFS(x) FALSE // TRACKREFS unused #define TRUELENGTH(x) (((((VECSEXP) (x))->vecsxp.length) == -1) ? R_BadLongVector(x, \"/var/folders/t8/1ry582nx6438y8pn6gk20f3c0000gn/T/preprocessor_test2233054488227688894.cpp\", 1341) : (((VECSEXP) (x))->vecsxp.truelength)) // TRUELENGTH used 37 times in data.table #define TYPEOF(x) ((x)->sxpinfo.type) // TYPEOF used 2832 times in 195 packages #define TYPE_BITS 5 // TYPE_BITS used 2 times in dplyr #define UNPROTECT(n) Rf_unprotect(n) // UNPROTECT used 12247 times in 758 packages #define UNPROTECT_PTR(s) Rf_unprotect_ptr(s) // UNPROTECT_PTR used 307 times in 14 packages #define UNSET_DDVAL_BIT(x) (((x)->sxpinfo.gp) &= ~1) // UNSET_DDVAL_BIT unused #define UNSET_S4_OBJECT(x) (((x)->sxpinfo.gp) &= ~((unsigned short)(1<<4))) // UNSET_S4_OBJECT used 2 times in data.table, slam #define VECSXP 19 // VECSXP used 3142 times in 385 packages #define VECTOR_ELT(x,i) ((SEXP *) (((SEXPREC_ALIGN *) (x)) + 1))[i] // VECTOR_ELT used 8626 times in 291 packages #define VECTOR_PTR(x) ((SEXP *) (((SEXPREC_ALIGN *) (x)) + 1)) // VECTOR_PTR used 17 times in bit, AdaptFitOS, RJSONIO, Rcpp11, bit64, Rcpp, locfit, iotools #define VectorToPairList Rf_VectorToPairList // VectorToPairList used 13 times in pomp, arules #define WEAKREFSXP 23 // WEAKREFSXP used 19 times in seqminer, Rcpp, pryr, rtkpp, rtkore, Rcpp11 #define XLENGTH(x) (((((VECSEXP) (x))->vecsxp.length) == -1) ? ((R_long_vec_hdr_t *) (x))[-1].lv_length : (((VECSEXP) (x))->vecsxp.length)) // XLENGTH used 287 times in 21 packages #define XTRUELENGTH(x) (((((VECSEXP) (x))->vecsxp.length) == -1) ? ((R_long_vec_hdr_t *) (x))[-1].lv_truelength : (((VECSEXP) (x))->vecsxp.truelength)) // XTRUELENGTH unused #define acopy_string Rf_acopy_string // acopy_string used 10 times in splusTimeDate #define addMissingVarsToNewEnv Rf_addMissingVarsToNewEnv // addMissingVarsToNewEnv unused #define alloc3DArray Rf_alloc3DArray // alloc3DArray used 21 times in mcmc, msm, TPmsm, unfoldr, RandomFields, cplm #define allocArray Rf_allocArray // allocArray used 24 times in unfoldr, kergp, pomp, proxy, kza, slam, mvMORPH, TPmsm, ouch, RandomFields #define allocFormalsList2 Rf_allocFormalsList2 // allocFormalsList2 unused #define allocFormalsList3 Rf_allocFormalsList3 // allocFormalsList3 unused #define allocFormalsList4 Rf_allocFormalsList4 // allocFormalsList4 unused #define allocFormalsList5 Rf_allocFormalsList5 // allocFormalsList5 unused #define allocFormalsList6 Rf_allocFormalsList6 // allocFormalsList6 unused #define allocList Rf_allocList // allocList used 60 times in 25 packages #define allocMatrix Rf_allocMatrix // allocMatrix used 1577 times in 244 packages #define allocS4Object Rf_allocS4Object // allocS4Object used 1 times in arules #define allocSExp Rf_allocSExp // allocSExp used 14 times in igraph, rgp, data.table, RandomFields, mmap, qtbase #define allocVector Rf_allocVector // allocVector used 12419 times in 551 packages #define allocVector3 Rf_allocVector3 // allocVector3 unused #define any_duplicated Rf_any_duplicated // any_duplicated used 5 times in data.table, checkmate #define any_duplicated3 Rf_any_duplicated3 // any_duplicated3 unused #define applyClosure Rf_applyClosure // applyClosure unused #define arraySubscript Rf_arraySubscript // arraySubscript used 13 times in proxy, arules, arulesSequences, cba, seriation #define asChar Rf_asChar // asChar used 194 times in 36 packages #define asCharacterFactor Rf_asCharacterFactor // asCharacterFactor used 11 times in fastmatch, Kmisc, data.table #define asComplex Rf_asComplex // asComplex used 1 times in ff #define asInteger Rf_asInteger // asInteger used 1277 times in 140 packages #define asLogical Rf_asLogical // asLogical used 462 times in 64 packages #define asReal Rf_asReal // asReal used 383 times in 83 packages #define asS4 Rf_asS4 // asS4 unused #define cPsort Rf_cPsort // cPsort unused #define classgets Rf_classgets // classgets used 91 times in 30 packages #define coerceVector Rf_coerceVector // coerceVector used 2585 times in 167 packages #define conformable Rf_conformable // conformable used 141 times in 22 packages #define cons Rf_cons // cons used 609 times in 39 packages #define copyListMatrix Rf_copyListMatrix // copyListMatrix used 1 times in Matrix #define copyMatrix Rf_copyMatrix // copyMatrix used 7 times in BDgraph, Matrix, kza #define copyMostAttrib Rf_copyMostAttrib // copyMostAttrib used 68 times in arules, robustbase, data.table, xts, memisc, proxy, zoo, tau #define copyVector Rf_copyVector // copyVector used 12 times in tm, kza, mlegp, adaptivetau #define countContexts Rf_countContexts // countContexts unused #define defineVar Rf_defineVar // defineVar used 218 times in 38 packages #define dimgets Rf_dimgets // dimgets used 3 times in CorrBin #define dimnamesgets Rf_dimnamesgets // dimnamesgets used 24 times in Matrix, RxCEcolInf, lxb, sapa #define duplicate Rf_duplicate // duplicate used 2088 times in 224 packages #define duplicated Rf_duplicated // duplicated used 402 times in 100 packages #define elt Rf_elt // elt used 2310 times in 37 packages #define error Rf_error // error used 63771 times in 1109 packages #define error_return(msg) { Rf_error(msg); return R_NilValue; } // error_return used 100 times in rpg, RPostgreSQL, Rook, git2r, grr, rJava, rmumps #define errorcall Rf_errorcall // errorcall used 103 times in RCurl, arules, XML, arulesSequences, pbdMPI, xts, proxy, cba, rJava, RSAP #define errorcall_return(cl,msg) { Rf_errorcall(cl, msg); return R_NilValue; } // errorcall_return used 31 times in Runuran #define eval Rf_eval // eval used 25178 times in 269 packages #define findFun Rf_findFun // findFun used 13 times in sprint, tikzDevice, yaml, unfoldr, TraMineR, RGtk2 #define findVar Rf_findVar // findVar used 1333 times in 24 packages #define findVarInFrame Rf_findVarInFrame // findVarInFrame used 101 times in 13 packages #define findVarInFrame3 Rf_findVarInFrame3 // findVarInFrame3 used 5 times in datamap #define getAttrib Rf_getAttrib // getAttrib used 1930 times in 239 packages #define getCharCE Rf_getCharCE // getCharCE used 16 times in ore, RSclient, PythonInR, Rserve, jsonlite, tau, rJava #define gsetVar Rf_gsetVar // gsetVar used 4 times in RSVGTipsDevice, Cairo, RSvgDevice, JavaGD #define iPsort Rf_iPsort // iPsort used 3 times in matrixStats, robustbase #define inherits Rf_inherits // inherits used 814 times in 80 packages #define install Rf_install // install used 3178 times in 224 packages #define installChar Rf_installChar // installChar used 4 times in dplyr #define installDDVAL Rf_installDDVAL // installDDVAL unused #define installS3Signature Rf_installS3Signature // installS3Signature unused #define isArray Rf_isArray // isArray used 34 times in checkmate, PythonInR, data.table, ifultools, Rblpapi, Rvcg, unfoldr, TMB, kza, qtbase #define isBasicClass Rf_isBasicClass // isBasicClass unused #define isBlankString Rf_isBlankString // isBlankString used 1 times in iotools #define isByteCode(x) (((x)->sxpinfo.type)==21) // isByteCode unused #define isComplex(s) (((s)->sxpinfo.type) == 15) // isComplex used 119 times in checkmate, PythonInR, ifultools, Rblpapi, Rcpp11, rmatio, stringi, Matrix, qtbase #define isEnvironment(s) (((s)->sxpinfo.type) == 4) // isEnvironment used 113 times in 52 packages #define isExpression(s) (((s)->sxpinfo.type) == 20) // isExpression used 3 times in PythonInR, Rcpp11 #define isFactor Rf_isFactor // isFactor used 42 times in checkmate, rggobi, PythonInR, data.table, Kmisc, partykit, cba, qtbase, RSQLite #define isFrame Rf_isFrame // isFrame used 15 times in checkmate, splusTimeDate, OjaNP, PythonInR, data.table, robfilter #define isFree Rf_isFree // isFree unused #define isFunction Rf_isFunction // isFunction used 274 times in 43 packages #define isInteger Rf_isInteger // isInteger used 402 times in 77 packages #define isLanguage Rf_isLanguage // isLanguage used 63 times in PythonInR, rgp, RandomFields #define isList Rf_isList // isList used 40 times in 11 packages #define isLogical(s) (((s)->sxpinfo.type) == 10) // isLogical used 215 times in 53 packages #define isMatrix Rf_isMatrix // isMatrix used 293 times in 65 packages #define isNewList Rf_isNewList // isNewList used 103 times in 27 packages #define isNull(s) (((s)->sxpinfo.type) == 0) // isNull used 1915 times in 119 packages #define isNumber Rf_isNumber // isNumber used 14 times in PythonInR, readr, stringi, qtbase #define isNumeric Rf_isNumeric // isNumeric used 468 times in 49 packages #define isObject(s) (((s)->sxpinfo.obj) != 0) // isObject used 11 times in dplyr, Rcpp, PythonInR, Rcpp11, stringi, rmumps #define isOrdered Rf_isOrdered // isOrdered used 65 times in partykit, PythonInR, data.table, RSQLite #define isPairList Rf_isPairList // isPairList used 2 times in PythonInR #define isPrimitive Rf_isPrimitive // isPrimitive used 7 times in PythonInR, qtbase #define isReal(s) (((s)->sxpinfo.type) == 14) // isReal used 323 times in 64 packages #define isS4 Rf_isS4 // isS4 used 13 times in PythonInR, Rcpp11, dplyr, Rcpp, catnet, rmumps, sdnet #define isString(s) (((s)->sxpinfo.type) == 16) // isString used 280 times in 59 packages #define isSymbol(s) (((s)->sxpinfo.type) == 1) // isSymbol used 68 times in PythonInR, data.table, Rcpp11, stringi, rgp, dbarts, rJava, sourcetools #define isTs Rf_isTs // isTs used 2 times in PythonInR #define isUnordered Rf_isUnordered // isUnordered used 2 times in PythonInR #define isUnsorted Rf_isUnsorted // isUnsorted unused #define isUserBinop Rf_isUserBinop // isUserBinop used 2 times in PythonInR #define isValidString Rf_isValidString // isValidString used 26 times in SSN, PythonInR, foreign, pbdMPI, RJSONIO, SASxport #define isValidStringF Rf_isValidStringF // isValidStringF used 2 times in PythonInR #define isVector Rf_isVector // isVector used 182 times in 46 packages #define isVectorAtomic Rf_isVectorAtomic // isVectorAtomic used 40 times in bit, matrixStats, checkmate, PythonInR, data.table, Matrix, bit64, potts, aster2, qtbase #define isVectorList Rf_isVectorList // isVectorList used 12 times in RPostgreSQL, spsurvey, PythonInR, stringi, adaptivetau, PCICt, RandomFields #define isVectorizable Rf_isVectorizable // isVectorizable used 3 times in PythonInR, robfilter #define lang1 Rf_lang1 // lang1 used 30 times in 11 packages #define lang2 Rf_lang2 // lang2 used 216 times in 75 packages #define lang3 Rf_lang3 // lang3 used 107 times in 28 packages #define lang4 Rf_lang4 // lang4 used 65 times in 21 packages #define lang5 Rf_lang5 // lang5 used 11 times in PBSddesolve, GNE, SMC #define lang6 Rf_lang6 // lang6 used 2 times in GNE #define lastElt Rf_lastElt // lastElt unused #define lazy_duplicate Rf_lazy_duplicate // lazy_duplicate unused #define lcons Rf_lcons // lcons used 16 times in rmgarch #define length(x) Rf_length(x) // length used 44060 times in 1224 packages #define lengthgets Rf_lengthgets // lengthgets used 47 times in 11 packages #define list1 Rf_list1 // list1 used 197 times in 11 packages #define list2 Rf_list2 // list2 used 441 times in 12 packages #define list3 Rf_list3 // list3 used 72 times in marked, Rdsdp, BH, svd #define list4 Rf_list4 // list4 used 58 times in igraph, PBSddesolve, Rserve, BH, yaml, treethresh, SMC #define list5 Rf_list5 // list5 used 63 times in Rdsdp, BH #define listAppend Rf_listAppend // listAppend used 1 times in ore #define match Rf_match // match used 8773 times in 388 packages #define matchE Rf_matchE // matchE unused #define mkChar Rf_mkChar // mkChar used 4545 times in 287 packages #define mkCharCE Rf_mkCharCE // mkCharCE used 72 times in 15 packages #define mkCharLen Rf_mkCharLen // mkCharLen used 38 times in 16 packages #define mkCharLenCE Rf_mkCharLenCE // mkCharLenCE used 23 times in 11 packages #define mkNamed Rf_mkNamed // mkNamed used 12 times in RCassandra, coxme, SamplerCompare, survival, JavaGD, DEoptim, qtbase #define mkString Rf_mkString // mkString used 814 times in 96 packages #define namesgets Rf_namesgets // namesgets used 80 times in 14 packages #define ncols Rf_ncols // ncols used 3805 times in 182 packages #define nlevels Rf_nlevels // nlevels used 546 times in 26 packages #define nrows Rf_nrows // nrows used 4332 times in 215 packages #define nthcdr Rf_nthcdr // nthcdr used 9 times in sprint, rmongodb, PythonInR, xts #define pmatch Rf_pmatch // pmatch used 169 times in ore, git2r, AdaptFitOS, data.table, seqminer, locfit, oce, rmumps #define protect Rf_protect // protect used 599 times in 101 packages #define psmatch Rf_psmatch // psmatch used 5 times in rgl #define rPsort Rf_rPsort // rPsort used 63 times in 15 packages #define reEnc Rf_reEnc // reEnc used 3 times in PythonInR, RJSONIO #define readS3VarsFromFrame Rf_readS3VarsFromFrame // readS3VarsFromFrame unused #define revsort Rf_revsort // revsort used 60 times in 20 packages #define rownamesgets Rf_rownamesgets // rownamesgets unused #define setAttrib Rf_setAttrib // setAttrib used 1830 times in 251 packages #define setIVector Rf_setIVector // setIVector unused #define setRVector Rf_setRVector // setRVector used 3 times in RcppClassic, RcppClassicExamples #define setSVector Rf_setSVector // setSVector unused #define setVar Rf_setVar // setVar used 24 times in Rhpc, rscproxy, PythonInR, rgenoud, survival, gsl, littler, spatstat #define shallow_duplicate Rf_shallow_duplicate // shallow_duplicate used 2 times in tmlenet, smint #define str2type Rf_str2type // str2type used 1 times in RGtk2 #define stringPositionTr Rf_stringPositionTr // stringPositionTr unused #define stringSuffix Rf_stringSuffix // stringSuffix unused #define substitute Rf_substitute // substitute used 255 times in 56 packages #define topenv Rf_topenv // topenv unused #define translateChar Rf_translateChar // translateChar used 59 times in 19 packages #define translateChar0 Rf_translateChar0 // translateChar0 unused #define translateCharUTF8 Rf_translateCharUTF8 // translateCharUTF8 used 66 times in 13 packages #define type2char Rf_type2char // type2char used 107 times in 12 packages #define type2rstr Rf_type2rstr // type2rstr unused #define type2str Rf_type2str // type2str used 3 times in Kmisc, yaml #define type2str_nowarn Rf_type2str_nowarn // type2str_nowarn used 1 times in qrmtools #define unprotect Rf_unprotect // unprotect used 110 times in 35 packages #define unprotect_ptr Rf_unprotect_ptr // unprotect_ptr unused #define warning Rf_warning // warning used 7679 times in 434 packages #define warningcall Rf_warningcall // warningcall used 4 times in RInside, jsonlite, pbdMPI #define warningcall_immediate Rf_warningcall_immediate // warningcall_immediate used 2 times in Runuran #define xlength(x) Rf_xlength(x) // xlength used 186 times in stringdist, yuima, matrixStats, Rhpc, validate, checkmate, dplR, Rdsdp, pscl, DescTools #define xlengthgets Rf_xlengthgets // xlengthgets unused # /Users/ls/Source/git/fastr/com.oracle.truffle.r.native/include/R_ext/Arith.h extern "C" { extern double R_NaN; // R_NaN used 469 times in 68 packages extern double R_PosInf; // R_PosInf used 562 times in 112 packages extern double R_NegInf; // R_NegInf used 699 times in 105 packages extern double R_NaReal; // R_NaReal used 140 times in 34 packages // NA_REAL used 1667 times in 226 packages extern int R_NaInt; // R_NaInt used 58 times in 20 packages // NA_INTEGER used 1520 times in 183 packages // NA_LOGICAL used 355 times in 73 packages int R_IsNA(double); // R_IsNA used 161 times in 40 packages int R_IsNaN(double); // R_IsNaN used 75 times in 28 packages int R_finite(double); // R_finite used 232 times in 44 packages int R_isnancpp(double); // R_isnancpp used 8 times in igraph, PwrGSD } # /Users/ls/Source/git/fastr/com.oracle.truffle.r.native/include/R_ext/Boolean.h extern "C" { typedef enum { FALSE = 0, TRUE } Rboolean; } # /Users/ls/Source/git/fastr/com.oracle.truffle.r.native/include/R_ext/Complex.h extern "C" { typedef struct { double r; double i; } Rcomplex; // Rcomplex used 893 times in 47 packages } # /Users/ls/Source/git/fastr/com.oracle.truffle.r.native/include/R_ext/Error.h extern "C" { void __attribute__((noreturn)) Rf_error(const char *, ...); void __attribute__((noreturn)) UNIMPLEMENTED(const char *); void __attribute__((noreturn)) WrongArgCount(const char *); void Rf_warning(const char *, ...); // Rf_warning used 316 times in 66 packages // warning used 7679 times in 434 packages void R_ShowMessage(const char *s); // R_ShowMessage used 104 times in Rserve, rJava, HiPLARM } # /Users/ls/Source/git/fastr/com.oracle.truffle.r.native/include/R_ext/Memory.h extern "C" { void* vmaxget(void); // vmaxget used 279 times in 20 packages void vmaxset(const void *); // vmaxset used 279 times in 20 packages void R_gc(void); // R_gc used 6 times in TMB, excel.link, gmatrix, microbenchmark int R_gc_running(); // R_gc_running unused char* R_alloc(size_t, int); // R_alloc used 7787 times in 330 packages long double *R_allocLD(size_t nelem); char* S_alloc(long, int); // S_alloc used 540 times in 50 packages char* S_realloc(char *, long, long, int); // S_realloc used 55 times in 11 packages } # /Users/ls/Source/git/fastr/com.oracle.truffle.r.native/include/R_ext/Print.h extern "C" { void Rprintf(const char *, ...); // Rprintf used 33813 times in 729 packages void REprintf(const char *, ...); // REprintf used 2531 times in 135 packages } # /Users/ls/Source/git/fastr/com.oracle.truffle.r.native/include/R_ext/Utils.h extern "C" { void R_isort(int*, int); // R_isort used 45 times in 18 packages void R_rsort(double*, int); // R_rsort used 210 times in 29 packages void R_csort(Rcomplex*, int); // R_csort unused void rsort_with_index(double *, int *, int); // rsort_with_index used 77 times in 40 packages void Rf_revsort(double*, int*, int); // Rf_revsort unused // revsort used 60 times in 20 packages void Rf_iPsort(int*, int, int); // Rf_iPsort unused // iPsort used 3 times in matrixStats, robustbase void Rf_rPsort(double*, int, int); // Rf_rPsort unused // rPsort used 63 times in 15 packages void Rf_cPsort(Rcomplex*, int, int); // Rf_cPsort unused // cPsort unused void R_qsort (double *v, size_t i, size_t j); // R_qsort used 10 times in extWeibQuant, pomp, robustbase, dplR, tclust, pcaPP void R_qsort_I (double *v, int *II, int i, int j); // R_qsort_I used 33 times in 15 packages void R_qsort_int (int *iv, size_t i, size_t j); // R_qsort_int unused void R_qsort_int_I(int *iv, int *II, int i, int j); // R_qsort_int_I used 19 times in ff, matrixStats, arules, Rborist, slam, eco, bnlearn const char *R_ExpandFileName(const char *); // R_ExpandFileName used 42 times in 20 packages void Rf_setIVector(int*, int, int); // Rf_setIVector unused // setIVector unused void Rf_setRVector(double*, int, double); // Rf_setRVector unused // setRVector used 3 times in RcppClassic, RcppClassicExamples Rboolean Rf_StringFalse(const char *); // Rf_StringFalse unused // StringFalse used 3 times in iotools Rboolean Rf_StringTrue(const char *); // Rf_StringTrue unused // StringTrue used 3 times in iotools Rboolean Rf_isBlankString(const char *); // Rf_isBlankString unused // isBlankString used 1 times in iotools double R_atof(const char *str); // R_atof used 9 times in SSN, tree, foreign, iotools double R_strtod(const char *c, char **end); // R_strtod used 4 times in ape, iotools char *R_tmpnam(const char *prefix, const char *tempdir); // R_tmpnam used 2 times in geometry char *R_tmpnam2(const char *prefix, const char *tempdir, const char *fileext); // R_tmpnam2 unused void R_CheckUserInterrupt(void); // R_CheckUserInterrupt used 1487 times in 234 packages void R_CheckStack(void); // R_CheckStack used 115 times in vcrpart, actuar, cplm, lme4, Matrix, GNE, randtoolbox, HiPLARM, rngWELL, pedigreemm void R_CheckStack2(size_t); // R_CheckStack2 unused int findInterval(double *xt, int n, double x, // findInterval used 11 times in BSquare, DNAprofiles, unfoldr, chebpol, pomp, eco, protViz, PBSmapping, spatstat Rboolean rightmost_closed, Rboolean all_inside, int ilo, int *mflag); void find_interv_vec(double *xt, int *n, double *x, int *nx, // find_interv_vec unused int *rightmost_closed, int *all_inside, int *indx); void R_max_col(double *matrix, int *nr, int *nc, int *maxes, int *ties_meth); // R_max_col used 2 times in geostatsp, MNP } # /Users/ls/Source/git/fastr/com.oracle.truffle.r.native/include/Rinternals.h extern "C" { typedef unsigned char Rbyte; typedef int R_len_t; // R_len_t used 2397 times in 70 packages typedef ptrdiff_t R_xlen_t; // R_xlen_t used 1537 times in 32 packages typedef struct { R_xlen_t lv_length, lv_truelength; } R_long_vec_hdr_t; typedef unsigned int SEXPTYPE; struct sxpinfo_struct { SEXPTYPE type : 5; unsigned int obj : 1; unsigned int named : 2; unsigned int gp : 16; unsigned int mark : 1; unsigned int debug : 1; unsigned int trace : 1; unsigned int spare : 1; unsigned int gcgen : 1; unsigned int gccls : 3; }; struct vecsxp_struct { R_len_t length; R_len_t truelength; }; struct primsxp_struct { int offset; }; struct symsxp_struct { struct SEXPREC *pname; struct SEXPREC *value; struct SEXPREC *internal; }; struct listsxp_struct { struct SEXPREC *carval; struct SEXPREC *cdrval; struct SEXPREC *tagval; }; struct envsxp_struct { struct SEXPREC *frame; struct SEXPREC *enclos; struct SEXPREC *hashtab; }; struct closxp_struct { struct SEXPREC *formals; struct SEXPREC *body; struct SEXPREC *env; }; struct promsxp_struct { struct SEXPREC *value; struct SEXPREC *expr; struct SEXPREC *env; }; typedef struct SEXPREC { struct sxpinfo_struct sxpinfo; struct SEXPREC *attrib; struct SEXPREC *gengc_next_node, *gengc_prev_node; union { struct primsxp_struct primsxp; struct symsxp_struct symsxp; struct listsxp_struct listsxp; struct envsxp_struct envsxp; struct closxp_struct closxp; struct promsxp_struct promsxp; } u; // u unused } SEXPREC, *SEXP; typedef struct VECTOR_SEXPREC { struct sxpinfo_struct sxpinfo; struct SEXPREC *attrib; struct SEXPREC *gengc_next_node, *gengc_prev_node; struct vecsxp_struct vecsxp; } VECTOR_SEXPREC, *VECSEXP; typedef union { VECTOR_SEXPREC s; double align; } SEXPREC_ALIGN; R_len_t __attribute__((noreturn)) R_BadLongVector(SEXP, const char *, int); SEXP (ATTRIB)(SEXP x); // ATTRIB used 83 times in 20 packages int (OBJECT)(SEXP x); // OBJECT used 102 times in 28 packages int (MARK)(SEXP x); // MARK used 251 times in 21 packages int (TYPEOF)(SEXP x); // TYPEOF used 2832 times in 195 packages int (NAMED)(SEXP x); // NAMED used 62 times in 22 packages int (REFCNT)(SEXP x); // REFCNT unused void (SET_OBJECT)(SEXP x, int v); // SET_OBJECT used 32 times in RSclient, reshape2, Rserve, data.table, actuar, dplyr, proxy, rmongodb, slam, tau void (SET_TYPEOF)(SEXP x, int v); // SET_TYPEOF used 38 times in 21 packages void (SET_NAMED)(SEXP x, int v); // SET_NAMED used 10 times in dplyr, yaml, data.table, iotools, RSQLite void SET_ATTRIB(SEXP x, SEXP v); // SET_ATTRIB used 54 times in 18 packages void DUPLICATE_ATTRIB(SEXP to, SEXP from); // DUPLICATE_ATTRIB used 5 times in covr, lfe, testthat, data.table int (IS_S4_OBJECT)(SEXP x); // IS_S4_OBJECT used 23 times in Rmosek, Runuran, data.table, xts, Matrix, slam, zoo, HiPLARM, OpenMx, tau void (SET_S4_OBJECT)(SEXP x); // SET_S4_OBJECT used 12 times in RSclient, redland, Rserve, data.table, FREGAT, rJPSGCS, tau void (UNSET_S4_OBJECT)(SEXP x); // UNSET_S4_OBJECT used 2 times in data.table, slam int (LENGTH)(SEXP x); // LENGTH used 5845 times in 356 packages int (TRUELENGTH)(SEXP x); // TRUELENGTH used 37 times in data.table void (SETLENGTH)(SEXP x, int v); // SETLENGTH used 65 times in 11 packages void (SET_TRUELENGTH)(SEXP x, int v); // SET_TRUELENGTH used 26 times in data.table R_xlen_t (XLENGTH)(SEXP x); // XLENGTH used 287 times in 21 packages R_xlen_t (XTRUELENGTH)(SEXP x); // XTRUELENGTH unused int (IS_LONG_VEC)(SEXP x); // IS_LONG_VEC used 1 times in RProtoBuf int (LEVELS)(SEXP x); // LEVELS used 18 times in rtdists, rPref, BsMD, data.table, stringi, dplyr, OBsMD, pbdZMQ, astrochron, RandomFields int (SETLEVELS)(SEXP x, int v); // SETLEVELS used 2 times in Rcpp11 int *(LOGICAL)(SEXP x); // LOGICAL used 4473 times in 288 packages int *(INTEGER)(SEXP x); // INTEGER used 41659 times in 758 packages Rbyte *(RAW)(SEXP x); // RAW used 880 times in 99 packages double *(REAL)(SEXP x); // REAL used 30947 times in 687 packages Rcomplex *(COMPLEX)(SEXP x); // COMPLEX used 1697 times in 71 packages SEXP (STRING_ELT)(SEXP x, R_xlen_t i); // STRING_ELT used 4143 times in 333 packages SEXP (VECTOR_ELT)(SEXP x, R_xlen_t i); // VECTOR_ELT used 8626 times in 291 packages void SET_STRING_ELT(SEXP x, R_xlen_t i, SEXP v); // SET_STRING_ELT used 5834 times in 321 packages SEXP SET_VECTOR_ELT(SEXP x, R_xlen_t i, SEXP v); // SET_VECTOR_ELT used 9751 times in 391 packages SEXP *(STRING_PTR)(SEXP x); // STRING_PTR used 65 times in 14 packages SEXP * __attribute__((noreturn)) (VECTOR_PTR)(SEXP x); SEXP (TAG)(SEXP e); // TAG used 513 times in 40 packages SEXP (CAR)(SEXP e); // CAR used 575 times in 63 packages SEXP (CDR)(SEXP e); // CDR used 4523 times in 76 packages SEXP (CAAR)(SEXP e); // CAAR unused SEXP (CDAR)(SEXP e); // CDAR unused SEXP (CADR)(SEXP e); // CADR used 104 times in 17 packages SEXP (CDDR)(SEXP e); // CDDR used 52 times in Rlabkey, Rcpp11, dplyr, proxy, Rcpp, slam, tikzDevice, OpenCL, svd SEXP (CDDDR)(SEXP e); // CDDDR unused SEXP (CADDR)(SEXP e); // CADDR used 52 times in 11 packages SEXP (CADDDR)(SEXP e); // CADDDR used 21 times in RPostgreSQL, foreign, actuar, bibtex SEXP (CAD4R)(SEXP e); // CAD4R used 14 times in earth, foreign, actuar int (MISSING)(SEXP x); // MISSING used 125 times in 25 packages void (SET_MISSING)(SEXP x, int v); // SET_MISSING used 1 times in sprint void SET_TAG(SEXP x, SEXP y); // SET_TAG used 200 times in 34 packages SEXP SETCAR(SEXP x, SEXP y); // SETCAR used 4072 times in 47 packages SEXP SETCDR(SEXP x, SEXP y); // SETCDR used 46 times in 14 packages SEXP SETCADR(SEXP x, SEXP y); // SETCADR used 112 times in 37 packages SEXP SETCADDR(SEXP x, SEXP y); // SETCADDR used 45 times in 14 packages SEXP SETCADDDR(SEXP x, SEXP y); // SETCADDDR used 31 times in 12 packages SEXP SETCAD4R(SEXP e, SEXP y); // SETCAD4R used 15 times in kergp, Sim.DiffProc, tikzDevice SEXP CONS_NR(SEXP a, SEXP b); // CONS_NR unused SEXP (FORMALS)(SEXP x); // FORMALS used 15 times in qtpaint, RSclient, PBSddesolve, Rserve, covr, pryr, rgp, testthat, RandomFields SEXP (BODY)(SEXP x); // BODY used 48 times in 15 packages SEXP (CLOENV)(SEXP x); // CLOENV used 23 times in Rcpp11, covr, pomp, Rcpp, pryr, testthat, qtbase int (RDEBUG)(SEXP x); // RDEBUG used 69 times in rmetasim int (RSTEP)(SEXP x); // RSTEP unused int (RTRACE)(SEXP x); // RTRACE unused void (SET_RDEBUG)(SEXP x, int v); // SET_RDEBUG unused void (SET_RSTEP)(SEXP x, int v); // SET_RSTEP unused void (SET_RTRACE)(SEXP x, int v); // SET_RTRACE unused void SET_FORMALS(SEXP x, SEXP v); // SET_FORMALS used 5 times in covr, rgp, testthat, qtbase void SET_BODY(SEXP x, SEXP v); // SET_BODY used 6 times in covr, rgp, testthat, qtbase void SET_CLOENV(SEXP x, SEXP v); // SET_CLOENV used 6 times in covr, rgp, testthat, qtbase SEXP (PRINTNAME)(SEXP x); // PRINTNAME used 92 times in 29 packages SEXP (SYMVALUE)(SEXP x); // SYMVALUE unused SEXP (INTERNAL)(SEXP x); // INTERNAL used 1014 times in 63 packages int (DDVAL)(SEXP x); // DDVAL unused void (SET_DDVAL)(SEXP x, int v); // SET_DDVAL unused void SET_PRINTNAME(SEXP x, SEXP v); // SET_PRINTNAME unused void SET_SYMVALUE(SEXP x, SEXP v); // SET_SYMVALUE unused void SET_INTERNAL(SEXP x, SEXP v); // SET_INTERNAL unused SEXP (FRAME)(SEXP x); // FRAME used 19 times in deTestSet, IRISSeismic, pryr, BayesBridge, datamap, BayesLogit SEXP (ENCLOS)(SEXP x); // ENCLOS used 7 times in Rcpp, pryr, rJava, Rcpp11, RGtk2 SEXP (HASHTAB)(SEXP x); // HASHTAB used 12 times in Rcpp, pryr, datamap, Rcpp11, qtbase int (ENVFLAGS)(SEXP x); // ENVFLAGS unused void (SET_ENVFLAGS)(SEXP x, int v); // SET_ENVFLAGS unused void SET_FRAME(SEXP x, SEXP v); // SET_FRAME used 4 times in rgp, mmap, qtbase void SET_ENCLOS(SEXP x, SEXP v); // SET_ENCLOS used 7 times in rgp, RandomFields, mmap, qtbase void SET_HASHTAB(SEXP x, SEXP v); // SET_HASHTAB used 5 times in rgp, mmap, qtbase SEXP (PRCODE)(SEXP x); // PRCODE used 15 times in dplyr, Rcpp, pryr, Rcpp11 SEXP (PRENV)(SEXP x); // PRENV used 14 times in igraph, dplyr, Rcpp, pryr, Rcpp11, lazyeval SEXP (PRVALUE)(SEXP x); // PRVALUE used 12 times in dplyr, Rcpp, pryr, Rcpp11 int (PRSEEN)(SEXP x); // PRSEEN used 4 times in Rcpp, Rcpp11 void (SET_PRSEEN)(SEXP x, int v); // SET_PRSEEN unused void SET_PRENV(SEXP x, SEXP v); // SET_PRENV unused void SET_PRVALUE(SEXP x, SEXP v); // SET_PRVALUE unused void SET_PRCODE(SEXP x, SEXP v); // SET_PRCODE unused void SET_PRSEEN(SEXP x, int v); // SET_PRSEEN unused int (HASHASH)(SEXP x); // HASHASH unused int (HASHVALUE)(SEXP x); // HASHVALUE unused void (SET_HASHASH)(SEXP x, int v); // SET_HASHASH unused void (SET_HASHVALUE)(SEXP x, int v); // SET_HASHVALUE unused typedef int PROTECT_INDEX; // PROTECT_INDEX used 94 times in 27 packages extern SEXP R_GlobalEnv; // R_GlobalEnv used 1400 times in 79 packages extern SEXP R_EmptyEnv; // R_EmptyEnv used 16 times in Rserve, dplR, Rcpp11, Rcpp, RcppClassic, pryr, rJava, adaptivetau, qtbase extern SEXP R_BaseEnv; // R_BaseEnv used 27 times in 15 packages extern SEXP R_BaseNamespace; // R_BaseNamespace used 3 times in Rcpp, Rcpp11 extern SEXP R_NamespaceRegistry; // R_NamespaceRegistry used 3 times in devtools, namespace, Rcpp extern SEXP R_Srcref; // R_Srcref unused extern SEXP R_NilValue; // R_NilValue used 10178 times in 491 packages extern SEXP R_UnboundValue; // R_UnboundValue used 73 times in 23 packages extern SEXP R_MissingArg; // R_MissingArg used 21 times in 12 packages extern SEXP R_RestartToken; // R_RestartToken unused extern SEXP R_baseSymbol; // R_baseSymbol unused extern SEXP R_BaseSymbol; // R_BaseSymbol unused extern SEXP R_BraceSymbol; // R_BraceSymbol unused extern SEXP R_Bracket2Symbol; // R_Bracket2Symbol used 4 times in purrr extern SEXP R_BracketSymbol; // R_BracketSymbol unused extern SEXP R_ClassSymbol; // R_ClassSymbol used 311 times in 84 packages extern SEXP R_DeviceSymbol; // R_DeviceSymbol unused extern SEXP R_DimNamesSymbol; // R_DimNamesSymbol used 230 times in 51 packages extern SEXP R_DimSymbol; // R_DimSymbol used 1015 times in 170 packages extern SEXP R_DollarSymbol; // R_DollarSymbol used 6 times in dplyr, Rcpp, Rcpp11 extern SEXP R_DotsSymbol; // R_DotsSymbol used 13 times in RPostgreSQL, RcppDE, lbfgs, purrr, RMySQL, DEoptim, qtbase extern SEXP R_DoubleColonSymbol; // R_DoubleColonSymbol unused extern SEXP R_DropSymbol; // R_DropSymbol unused extern SEXP R_LastvalueSymbol; // R_LastvalueSymbol unused extern SEXP R_LevelsSymbol; // R_LevelsSymbol used 51 times in 17 packages extern SEXP R_ModeSymbol; // R_ModeSymbol unused extern SEXP R_NaRmSymbol; // R_NaRmSymbol used 2 times in dplyr extern SEXP R_NameSymbol; // R_NameSymbol used 2 times in qtbase extern SEXP R_NamesSymbol; // R_NamesSymbol used 1373 times in 249 packages extern SEXP R_NamespaceEnvSymbol; // R_NamespaceEnvSymbol unused extern SEXP R_PackageSymbol; // R_PackageSymbol used 2 times in Rmosek, HiPLARM extern SEXP R_PreviousSymbol; // R_PreviousSymbol unused extern SEXP R_QuoteSymbol; // R_QuoteSymbol unused extern SEXP R_RowNamesSymbol; // R_RowNamesSymbol used 97 times in 37 packages extern SEXP R_SeedsSymbol; // R_SeedsSymbol used 2 times in treatSens extern SEXP R_SortListSymbol; // R_SortListSymbol unused extern SEXP R_SourceSymbol; // R_SourceSymbol unused extern SEXP R_SpecSymbol; // R_SpecSymbol unused extern SEXP R_TripleColonSymbol; // R_TripleColonSymbol unused extern SEXP R_TspSymbol; // R_TspSymbol unused extern SEXP R_dot_defined; // R_dot_defined unused extern SEXP R_dot_Method; // R_dot_Method unused extern SEXP R_dot_packageName; // R_dot_packageName unused extern SEXP R_dot_target; // R_dot_target unused extern SEXP R_NaString; // R_NaString used 36 times in stringdist, RCurl, RSclient, uniqueAtomMat, XML, Rserve, Rblpapi, SoundexBR, rJava, iotools // NA_STRING used 574 times in 90 packages extern SEXP R_BlankString; // R_BlankString used 39 times in 13 packages extern SEXP R_BlankScalarString; // R_BlankScalarString unused SEXP R_GetCurrentSrcref(int); // R_GetCurrentSrcref unused SEXP R_GetSrcFilename(SEXP); // R_GetSrcFilename unused SEXP Rf_asChar(SEXP); // Rf_asChar used 246 times in 16 packages // asChar used 194 times in 36 packages SEXP Rf_coerceVector(SEXP, SEXPTYPE); // Rf_coerceVector used 44 times in 13 packages // coerceVector used 2585 times in 167 packages SEXP Rf_PairToVectorList(SEXP x); // Rf_PairToVectorList unused // PairToVectorList used 7 times in cba, rcdd SEXP Rf_VectorToPairList(SEXP x); // Rf_VectorToPairList unused // VectorToPairList used 13 times in pomp, arules SEXP Rf_asCharacterFactor(SEXP x); // Rf_asCharacterFactor used 3 times in tidyr, reshape2, RSQLite // asCharacterFactor used 11 times in fastmatch, Kmisc, data.table int Rf_asLogical(SEXP x); // Rf_asLogical used 45 times in 11 packages // asLogical used 462 times in 64 packages int Rf_asInteger(SEXP x); // Rf_asInteger used 746 times in 23 packages // asInteger used 1277 times in 140 packages double Rf_asReal(SEXP x); // Rf_asReal used 113 times in 17 packages // asReal used 383 times in 83 packages Rcomplex Rf_asComplex(SEXP x); // Rf_asComplex unused // asComplex used 1 times in ff typedef struct R_allocator R_allocator_t; char * Rf_acopy_string(const char *); // Rf_acopy_string unused // acopy_string used 10 times in splusTimeDate void Rf_addMissingVarsToNewEnv(SEXP, SEXP); // Rf_addMissingVarsToNewEnv unused // addMissingVarsToNewEnv unused SEXP Rf_alloc3DArray(SEXPTYPE, int, int, int); // Rf_alloc3DArray unused // alloc3DArray used 21 times in mcmc, msm, TPmsm, unfoldr, RandomFields, cplm SEXP Rf_allocArray(SEXPTYPE, SEXP); // Rf_allocArray used 4 times in h5 // allocArray used 24 times in unfoldr, kergp, pomp, proxy, kza, slam, mvMORPH, TPmsm, ouch, RandomFields SEXP Rf_allocFormalsList2(SEXP sym1, SEXP sym2); // Rf_allocFormalsList2 unused // allocFormalsList2 unused SEXP Rf_allocFormalsList3(SEXP sym1, SEXP sym2, SEXP sym3); // Rf_allocFormalsList3 unused // allocFormalsList3 unused SEXP Rf_allocFormalsList4(SEXP sym1, SEXP sym2, SEXP sym3, SEXP sym4); // Rf_allocFormalsList4 unused // allocFormalsList4 unused SEXP Rf_allocFormalsList5(SEXP sym1, SEXP sym2, SEXP sym3, SEXP sym4, SEXP sym5); // Rf_allocFormalsList5 unused // allocFormalsList5 unused SEXP Rf_allocFormalsList6(SEXP sym1, SEXP sym2, SEXP sym3, SEXP sym4, SEXP sym5, SEXP sym6); // Rf_allocFormalsList6 unused // allocFormalsList6 unused SEXP Rf_allocMatrix(SEXPTYPE, int, int); // Rf_allocMatrix used 122 times in 14 packages // allocMatrix used 1577 times in 244 packages SEXP Rf_allocList(int); // Rf_allocList unused // allocList used 60 times in 25 packages SEXP Rf_allocS4Object(void); // Rf_allocS4Object used 2 times in Rserve, RSclient // allocS4Object used 1 times in arules SEXP Rf_allocSExp(SEXPTYPE); // Rf_allocSExp unused // allocSExp used 14 times in igraph, rgp, data.table, RandomFields, mmap, qtbase SEXP Rf_allocVector3(SEXPTYPE, R_xlen_t, R_allocator_t*); // Rf_allocVector3 unused // allocVector3 unused R_xlen_t Rf_any_duplicated(SEXP x, Rboolean from_last); // Rf_any_duplicated unused // any_duplicated used 5 times in data.table, checkmate R_xlen_t Rf_any_duplicated3(SEXP x, SEXP incomp, Rboolean from_last); // Rf_any_duplicated3 unused // any_duplicated3 unused SEXP Rf_applyClosure(SEXP, SEXP, SEXP, SEXP, SEXP); // Rf_applyClosure unused // applyClosure unused SEXP Rf_arraySubscript(int, SEXP, SEXP, SEXP (*)(SEXP,SEXP), SEXP (*)(SEXP, int), SEXP); SEXP Rf_classgets(SEXP, SEXP); // Rf_classgets used 27 times in fts, clpAPI, cplexAPI, sybilSBML, Rblpapi, glpkAPI // classgets used 91 times in 30 packages SEXP Rf_cons(SEXP, SEXP); // Rf_cons used 39 times in dplyr, Rcpp, Rcpp11 // cons used 609 times in 39 packages void Rf_copyMatrix(SEXP, SEXP, Rboolean); // Rf_copyMatrix used 8 times in CNVassoc // copyMatrix used 7 times in BDgraph, Matrix, kza void Rf_copyListMatrix(SEXP, SEXP, Rboolean); // Rf_copyListMatrix unused // copyListMatrix used 1 times in Matrix void Rf_copyMostAttrib(SEXP, SEXP); // Rf_copyMostAttrib used 8 times in tidyr, purrr, Rcpp, reshape2 // copyMostAttrib used 68 times in arules, robustbase, data.table, xts, memisc, proxy, zoo, tau void Rf_copyVector(SEXP, SEXP); // Rf_copyVector unused // copyVector used 12 times in tm, kza, mlegp, adaptivetau int Rf_countContexts(int, int); // Rf_countContexts unused // countContexts unused SEXP Rf_CreateTag(SEXP); // Rf_CreateTag unused // CreateTag used 1 times in rgp void Rf_defineVar(SEXP, SEXP, SEXP); // Rf_defineVar used 7 times in purrr, Rcpp, Rserve, Rcpp11 // defineVar used 218 times in 38 packages SEXP Rf_dimgets(SEXP, SEXP); // Rf_dimgets unused // dimgets used 3 times in CorrBin SEXP Rf_dimnamesgets(SEXP, SEXP); // Rf_dimnamesgets unused // dimnamesgets used 24 times in Matrix, RxCEcolInf, lxb, sapa SEXP Rf_DropDims(SEXP); // Rf_DropDims unused // DropDims unused SEXP Rf_duplicate(SEXP); // Rf_duplicate used 21 times in XML, data.table, Rcpp11, lme4, dplyr, Rcpp, RcppClassic, grr, NMF, copula // duplicate used 2088 times in 224 packages SEXP Rf_shallow_duplicate(SEXP); // Rf_shallow_duplicate unused // shallow_duplicate used 2 times in tmlenet, smint SEXP Rf_lazy_duplicate(SEXP); // Rf_lazy_duplicate unused // lazy_duplicate unused SEXP Rf_duplicated(SEXP, Rboolean); // Rf_duplicated unused // duplicated used 402 times in 100 packages Rboolean R_envHasNoSpecialSymbols(SEXP); // R_envHasNoSpecialSymbols unused SEXP Rf_eval(SEXP, SEXP); // Rf_eval used 105 times in 24 packages // eval used 25178 times in 269 packages SEXP Rf_findFun(SEXP, SEXP); // Rf_findFun used 7 times in Rcpp, Rcpp11, littler, RGtk2 // findFun used 13 times in sprint, tikzDevice, yaml, unfoldr, TraMineR, RGtk2 SEXP Rf_findVar(SEXP, SEXP); // Rf_findVar used 19 times in R2SWF, Rcpp11, dplyr, Rcpp, pryr, rJava, littler, showtext // findVar used 1333 times in 24 packages SEXP Rf_findVarInFrame(SEXP, SEXP); // Rf_findVarInFrame used 7 times in RCurl, Rcpp, Rcpp11 // findVarInFrame used 101 times in 13 packages SEXP Rf_findVarInFrame3(SEXP, SEXP, Rboolean); // Rf_findVarInFrame3 used 1 times in pryr // findVarInFrame3 used 5 times in datamap SEXP Rf_getAttrib(SEXP, SEXP); // Rf_getAttrib used 256 times in 36 packages // getAttrib used 1930 times in 239 packages SEXP Rf_GetArrayDimnames(SEXP); // Rf_GetArrayDimnames unused // GetArrayDimnames unused SEXP Rf_GetColNames(SEXP); // Rf_GetColNames unused // GetColNames unused void Rf_GetMatrixDimnames(SEXP, SEXP*, SEXP*, const char**, const char**); // Rf_GetMatrixDimnames unused // GetMatrixDimnames used 2 times in Kmisc, optmatch SEXP Rf_GetOption(SEXP, SEXP); // Rf_GetOption unused // GetOption used 5 times in rgl, gmp, Cairo, RGtk2 SEXP Rf_GetOption1(SEXP); // Rf_GetOption1 used 5 times in RProtoBuf, gmp // GetOption1 used 1 times in PCICt int Rf_GetOptionDigits(void); // Rf_GetOptionDigits unused // GetOptionDigits unused int Rf_GetOptionWidth(void); // Rf_GetOptionWidth used 1 times in progress // GetOptionWidth unused SEXP Rf_GetRowNames(SEXP); // Rf_GetRowNames unused // GetRowNames unused void Rf_gsetVar(SEXP, SEXP, SEXP); // Rf_gsetVar unused // gsetVar used 4 times in RSVGTipsDevice, Cairo, RSvgDevice, JavaGD SEXP Rf_install(const char *); // Rf_install used 990 times in 50 packages // install used 3178 times in 224 packages SEXP Rf_installChar(SEXP); // Rf_installChar used 15 times in dplyr, Rcpp // installChar used 4 times in dplyr SEXP Rf_installDDVAL(int i); // Rf_installDDVAL unused // installDDVAL unused SEXP Rf_installS3Signature(const char *, const char *); // Rf_installS3Signature unused // installS3Signature unused Rboolean Rf_isFree(SEXP); // Rf_isFree unused // isFree unused Rboolean Rf_isOrdered(SEXP); // Rf_isOrdered unused // isOrdered used 65 times in partykit, PythonInR, data.table, RSQLite Rboolean Rf_isUnordered(SEXP); // Rf_isUnordered used 1 times in OpenMx // isUnordered used 2 times in PythonInR Rboolean Rf_isUnsorted(SEXP, Rboolean); // Rf_isUnsorted unused // isUnsorted unused SEXP Rf_lengthgets(SEXP, R_len_t); // Rf_lengthgets used 7 times in readxl, readr // lengthgets used 47 times in 11 packages SEXP Rf_xlengthgets(SEXP, R_xlen_t); // Rf_xlengthgets unused // xlengthgets unused SEXP R_lsInternal(SEXP, Rboolean); // R_lsInternal used 5 times in Rcpp, rJava, Rcpp11, qtbase SEXP R_lsInternal3(SEXP, Rboolean, Rboolean); // R_lsInternal3 unused SEXP Rf_match(SEXP, SEXP, int); // Rf_match used 2 times in Rvcg // match used 8773 times in 388 packages SEXP Rf_matchE(SEXP, SEXP, int, SEXP); // Rf_matchE unused // matchE unused SEXP Rf_namesgets(SEXP, SEXP); // Rf_namesgets used 4 times in OpenMx, rpf // namesgets used 80 times in 14 packages SEXP Rf_mkChar(const char *); // Rf_mkChar used 517 times in 32 packages // mkChar used 4545 times in 287 packages SEXP Rf_mkCharLen(const char *, int); // Rf_mkCharLen used 21 times in refGenome, redland, Rcpp11, stringi, Kmisc, Rcpp, sourcetools, iotools // mkCharLen used 38 times in 16 packages Rboolean Rf_NonNullStringMatch(SEXP, SEXP); // Rf_NonNullStringMatch unused // NonNullStringMatch used 8 times in proxy, arules, arulesSequences, cba int Rf_ncols(SEXP); // Rf_ncols used 22 times in fdaPDE, fts, BoomSpikeSlab, Rmosek, ccgarch, rcppbugs, biganalytics, CEC, OpenMx, RTriangle // ncols used 3805 times in 182 packages int Rf_nrows(SEXP); // Rf_nrows used 32 times in 12 packages // nrows used 4332 times in 215 packages SEXP Rf_nthcdr(SEXP, int); // Rf_nthcdr unused // nthcdr used 9 times in sprint, rmongodb, PythonInR, xts typedef enum {Bytes, Chars, Width} nchar_type; int R_nchar(SEXP string, nchar_type type_, // R_nchar unused Rboolean allowNA, Rboolean keepNA, const char* msg_name); Rboolean Rf_pmatch(SEXP, SEXP, Rboolean); // Rf_pmatch unused // pmatch used 169 times in ore, git2r, AdaptFitOS, data.table, seqminer, locfit, oce, rmumps Rboolean Rf_psmatch(const char *, const char *, Rboolean); // Rf_psmatch unused // psmatch used 5 times in rgl void Rf_PrintValue(SEXP); // Rf_PrintValue used 95 times in 19 packages // PrintValue used 119 times in 13 packages void Rf_readS3VarsFromFrame(SEXP, SEXP*, SEXP*, SEXP*, SEXP*, SEXP*, SEXP*); // Rf_readS3VarsFromFrame unused // readS3VarsFromFrame unused SEXP Rf_setAttrib(SEXP, SEXP, SEXP); // Rf_setAttrib used 325 times in 35 packages // setAttrib used 1830 times in 251 packages void Rf_setSVector(SEXP*, int, SEXP); // Rf_setSVector unused // setSVector unused void Rf_setVar(SEXP, SEXP, SEXP); // Rf_setVar used 1 times in showtext // setVar used 24 times in Rhpc, rscproxy, PythonInR, rgenoud, survival, gsl, littler, spatstat SEXP Rf_stringSuffix(SEXP, int); // Rf_stringSuffix unused // stringSuffix unused SEXPTYPE Rf_str2type(const char *); // Rf_str2type used 4 times in purrr // str2type used 1 times in RGtk2 Rboolean Rf_StringBlank(SEXP); // Rf_StringBlank used 1 times in LCMCR // StringBlank unused SEXP Rf_substitute(SEXP,SEXP); // Rf_substitute unused // substitute used 255 times in 56 packages const char * Rf_translateChar(SEXP); // Rf_translateChar used 1 times in devEMF // translateChar used 59 times in 19 packages const char * Rf_translateChar0(SEXP); // Rf_translateChar0 unused // translateChar0 unused const char * Rf_translateCharUTF8(SEXP); // Rf_translateCharUTF8 used 22 times in Rserve, xml2, readr, gdtools, Rcpp11, dplyr, Rcpp, haven // translateCharUTF8 used 66 times in 13 packages const char * Rf_type2char(SEXPTYPE); // Rf_type2char used 33 times in 13 packages // type2char used 107 times in 12 packages SEXP Rf_type2rstr(SEXPTYPE); // Rf_type2rstr unused // type2rstr unused SEXP Rf_type2str(SEXPTYPE); // Rf_type2str used 4 times in Rcpp, pryr // type2str used 3 times in Kmisc, yaml SEXP Rf_type2str_nowarn(SEXPTYPE); // Rf_type2str_nowarn unused // type2str_nowarn used 1 times in qrmtools void Rf_unprotect_ptr(SEXP); // Rf_unprotect_ptr unused // unprotect_ptr unused void __attribute__((noreturn)) R_signal_protect_error(void); void __attribute__((noreturn)) R_signal_unprotect_error(void); void __attribute__((noreturn)) R_signal_reprotect_error(PROTECT_INDEX i); SEXP R_tryEval(SEXP, SEXP, int *); // R_tryEval used 1118 times in 24 packages SEXP R_tryEvalSilent(SEXP, SEXP, int *); // R_tryEvalSilent unused const char *R_curErrorBuf(); // R_curErrorBuf used 4 times in Rhpc, Rcpp11 Rboolean Rf_isS4(SEXP); // Rf_isS4 used 16 times in Rcpp, Rcpp11 // isS4 used 13 times in PythonInR, Rcpp11, dplyr, Rcpp, catnet, rmumps, sdnet SEXP Rf_asS4(SEXP, Rboolean, int); // Rf_asS4 unused // asS4 unused SEXP Rf_S3Class(SEXP); // Rf_S3Class unused // S3Class used 4 times in RInside, littler int Rf_isBasicClass(const char *); // Rf_isBasicClass unused // isBasicClass unused Rboolean R_cycle_detected(SEXP s, SEXP child); // R_cycle_detected unused typedef enum { CE_NATIVE = 0, CE_UTF8 = 1, CE_LATIN1 = 2, CE_BYTES = 3, CE_SYMBOL = 5, CE_ANY =99 } cetype_t; // cetype_t used 47 times in 13 packages cetype_t Rf_getCharCE(SEXP); // Rf_getCharCE used 13 times in RSclient, Rserve, genie, dplyr, Rcpp, rJava, ROracle // getCharCE used 16 times in ore, RSclient, PythonInR, Rserve, jsonlite, tau, rJava SEXP Rf_mkCharCE(const char *, cetype_t); // Rf_mkCharCE used 40 times in readxl, mongolite, xml2, readr, Rcpp11, stringi, commonmark, dplyr, Rcpp, haven // mkCharCE used 72 times in 15 packages SEXP Rf_mkCharLenCE(const char *, int, cetype_t); // Rf_mkCharLenCE used 68 times in readr, ROracle, stringi // mkCharLenCE used 23 times in 11 packages const char *Rf_reEnc(const char *x, cetype_t ce_in, cetype_t ce_out, int subst); // Rf_reEnc used 5 times in RCurl, RSclient, Rserve, rJava // reEnc used 3 times in PythonInR, RJSONIO SEXP R_forceAndCall(SEXP e, int n, SEXP rho); // R_forceAndCall unused SEXP R_MakeExternalPtr(void *p, SEXP tag, SEXP prot); // R_MakeExternalPtr used 321 times in 102 packages void *R_ExternalPtrAddr(SEXP s); // R_ExternalPtrAddr used 2127 times in 115 packages SEXP R_ExternalPtrTag(SEXP s); // R_ExternalPtrTag used 195 times in 32 packages SEXP R_ExternalPtrProtected(SEXP s); // R_ExternalPtrProtected used 6 times in PopGenome, Rcpp, WhopGenome, data.table, Rcpp11 void R_ClearExternalPtr(SEXP s); // R_ClearExternalPtr used 157 times in 64 packages void R_SetExternalPtrAddr(SEXP s, void *p); // R_SetExternalPtrAddr used 23 times in ff, PopGenome, RCurl, rstream, Rlabkey, WhopGenome, XML, RJSONIO, memisc, ROracle void R_SetExternalPtrTag(SEXP s, SEXP tag); // R_SetExternalPtrTag used 16 times in PopGenome, rstream, Rlabkey, WhopGenome, Rcpp11, Rcpp, rLindo void R_SetExternalPtrProtected(SEXP s, SEXP p); // R_SetExternalPtrProtected used 9 times in PopGenome, rstream, Rlabkey, Rcpp, WhopGenome, Rcpp11 typedef void (*R_CFinalizer_t)(SEXP); void R_RegisterFinalizer(SEXP s, SEXP fun); // R_RegisterFinalizer used 1 times in XML void R_RegisterCFinalizer(SEXP s, R_CFinalizer_t fun); // R_RegisterCFinalizer used 73 times in 27 packages void R_RegisterFinalizerEx(SEXP s, SEXP fun, Rboolean onexit); // R_RegisterFinalizerEx unused void R_RegisterCFinalizerEx(SEXP s, R_CFinalizer_t fun, Rboolean onexit); // R_RegisterCFinalizerEx used 152 times in 58 packages void R_RunPendingFinalizers(void); // R_RunPendingFinalizers unused SEXP R_MakeWeakRef(SEXP key, SEXP val, SEXP fin, Rboolean onexit); // R_MakeWeakRef used 4 times in igraph, svd SEXP R_MakeWeakRefC(SEXP key, SEXP val, R_CFinalizer_t fin, Rboolean onexit); // R_MakeWeakRefC unused SEXP R_WeakRefKey(SEXP w); // R_WeakRefKey used 3 times in igraph, Rcpp, Rcpp11 SEXP R_WeakRefValue(SEXP w); // R_WeakRefValue used 7 times in igraph, Rcpp, svd, Rcpp11 void R_RunWeakRefFinalizer(SEXP w); // R_RunWeakRefFinalizer used 1 times in igraph SEXP R_PromiseExpr(SEXP); // R_PromiseExpr unused SEXP R_ClosureExpr(SEXP); // R_ClosureExpr unused void R_initialize_bcode(void); // R_initialize_bcode unused SEXP R_bcEncode(SEXP); // R_bcEncode unused SEXP R_bcDecode(SEXP); // R_bcDecode unused Rboolean R_ToplevelExec(void (*fun)(void *), void *data); SEXP R_ExecWithCleanup(SEXP (*fun)(void *), void *data, void (*cleanfun)(void *), void *cleandata); void R_RestoreHashCount(SEXP rho); // R_RestoreHashCount unused Rboolean R_IsPackageEnv(SEXP rho); // R_IsPackageEnv unused SEXP R_PackageEnvName(SEXP rho); // R_PackageEnvName unused SEXP R_FindPackageEnv(SEXP info); // R_FindPackageEnv unused Rboolean R_IsNamespaceEnv(SEXP rho); // R_IsNamespaceEnv unused SEXP R_NamespaceEnvSpec(SEXP rho); // R_NamespaceEnvSpec unused SEXP R_FindNamespace(SEXP info); // R_FindNamespace used 14 times in 11 packages void R_LockEnvironment(SEXP env, Rboolean bindings); // R_LockEnvironment used 2 times in Rcpp, Rcpp11 Rboolean R_EnvironmentIsLocked(SEXP env); // R_EnvironmentIsLocked used 2 times in Rcpp, Rcpp11 void R_LockBinding(SEXP sym, SEXP env); // R_LockBinding used 3 times in data.table, Rcpp, Rcpp11 void R_unLockBinding(SEXP sym, SEXP env); // R_unLockBinding used 2 times in Rcpp, Rcpp11 void R_MakeActiveBinding(SEXP sym, SEXP fun, SEXP env); // R_MakeActiveBinding unused Rboolean R_BindingIsLocked(SEXP sym, SEXP env); // R_BindingIsLocked used 2 times in Rcpp, Rcpp11 Rboolean R_BindingIsActive(SEXP sym, SEXP env); // R_BindingIsActive used 2 times in Rcpp, Rcpp11 Rboolean R_HasFancyBindings(SEXP rho); // R_HasFancyBindings unused void Rf_errorcall(SEXP, const char *, ...) __attribute__((noreturn)); // Rf_errorcall used 27 times in purrr, mongolite, jsonlite, pbdMPI, rJava, openssl // errorcall used 103 times in RCurl, arules, XML, arulesSequences, pbdMPI, xts, proxy, cba, rJava, RSAP void Rf_warningcall(SEXP, const char *, ...); // Rf_warningcall used 5 times in pbdMPI, mongolite // warningcall used 4 times in RInside, jsonlite, pbdMPI void Rf_warningcall_immediate(SEXP, const char *, ...); // Rf_warningcall_immediate used 2 times in mongolite, V8 // warningcall_immediate used 2 times in Runuran void R_XDREncodeDouble(double d, void *buf); // R_XDREncodeDouble unused double R_XDRDecodeDouble(void *buf); // R_XDRDecodeDouble unused void R_XDREncodeInteger(int i, void *buf); // R_XDREncodeInteger unused int R_XDRDecodeInteger(void *buf); // R_XDRDecodeInteger unused typedef void *R_pstream_data_t; typedef enum { R_pstream_any_format, R_pstream_ascii_format, R_pstream_binary_format, R_pstream_xdr_format, R_pstream_asciihex_format } R_pstream_format_t; // R_pstream_format_t used 7 times in RApiSerialize, Rhpc, fastdigest typedef struct R_outpstream_st *R_outpstream_t; struct R_outpstream_st { R_pstream_data_t data; R_pstream_format_t type; int version; void (*OutChar)(R_outpstream_t, int); void (*OutBytes)(R_outpstream_t, void *, int); SEXP (*OutPersistHookFunc)(SEXP, SEXP); SEXP OutPersistHookData; // OutPersistHookData unused }; typedef struct R_inpstream_st *R_inpstream_t; struct R_inpstream_st { R_pstream_data_t data; R_pstream_format_t type; int (*InChar)(R_inpstream_t); void (*InBytes)(R_inpstream_t, void *, int); SEXP (*InPersistHookFunc)(SEXP, SEXP); SEXP InPersistHookData; // InPersistHookData unused }; void R_InitInPStream(R_inpstream_t stream, R_pstream_data_t data, // R_InitInPStream used 2 times in RApiSerialize, Rhpc R_pstream_format_t type, int (*inchar)(R_inpstream_t), void (*inbytes)(R_inpstream_t, void *, int), SEXP (*phook)(SEXP, SEXP), SEXP pdata); void R_InitOutPStream(R_outpstream_t stream, R_pstream_data_t data, // R_InitOutPStream used 4 times in RApiSerialize, Rhpc, fastdigest, qtbase R_pstream_format_t type, int version, void (*outchar)(R_outpstream_t, int), void (*outbytes)(R_outpstream_t, void *, int), SEXP (*phook)(SEXP, SEXP), SEXP pdata); void R_InitFileInPStream(R_inpstream_t stream, FILE *fp, // R_InitFileInPStream used 1 times in filehash R_pstream_format_t type, SEXP (*phook)(SEXP, SEXP), SEXP pdata); void R_InitFileOutPStream(R_outpstream_t stream, FILE *fp, // R_InitFileOutPStream unused R_pstream_format_t type, int version, SEXP (*phook)(SEXP, SEXP), SEXP pdata); void R_Serialize(SEXP s, R_outpstream_t ops); // R_Serialize used 4 times in RApiSerialize, Rhpc, fastdigest, qtbase SEXP R_Unserialize(R_inpstream_t ips); // R_Unserialize used 4 times in RApiSerialize, Rhpc, filehash SEXP R_do_slot(SEXP obj, SEXP name); // R_do_slot used 162 times in 11 packages SEXP R_do_slot_assign(SEXP obj, SEXP name, SEXP value); // R_do_slot_assign used 17 times in excel.link, redland, Rcpp11, Matrix, TMB, Rcpp, FREGAT, HiPLARM, OpenMx, rJPSGCS int R_has_slot(SEXP obj, SEXP name); // R_has_slot used 14 times in Matrix, Rcpp, HiPLARM, OpenMx, Rcpp11 SEXP R_do_MAKE_CLASS(const char *what); // R_do_MAKE_CLASS used 6 times in TMB, Rcpp, Rcpp11 SEXP R_getClassDef (const char *what); // R_getClassDef used 5 times in memisc, Rcpp, Rcpp11 SEXP R_getClassDef_R(SEXP what); // R_getClassDef_R unused Rboolean R_has_methods_attached(void); // R_has_methods_attached unused Rboolean R_isVirtualClass(SEXP class_def, SEXP env); // R_isVirtualClass unused Rboolean R_extends (SEXP class1, SEXP class2, SEXP env); // R_extends unused SEXP R_do_new_object(SEXP class_def); // R_do_new_object used 9 times in TMB, memisc, Rcpp, Rcpp11 int R_check_class_and_super(SEXP x, const char **valid, SEXP rho); // R_check_class_and_super used 5 times in Matrix, Rmosek, HiPLARM int R_check_class_etc (SEXP x, const char **valid); // R_check_class_etc used 41 times in Matrix, HiPLARM void R_PreserveObject(SEXP); // R_PreserveObject used 112 times in 29 packages void R_ReleaseObject(SEXP); // R_ReleaseObject used 114 times in 27 packages void R_dot_Last(void); // R_dot_Last used 4 times in RInside, rJava, littler void R_RunExitFinalizers(void); // R_RunExitFinalizers used 4 times in RInside, TMB, rJava, littler int R_system(const char *); // R_system used 1 times in rJava Rboolean R_compute_identical(SEXP, SEXP, int); // R_compute_identical used 14 times in igraph, Matrix, rgp, data.table void R_orderVector(int *indx, int n, SEXP arglist, Rboolean nalast, Rboolean decreasing); // R_orderVector used 5 times in glpkAPI, nontarget, CEGO SEXP Rf_allocVector(SEXPTYPE, R_xlen_t); // Rf_allocVector used 1086 times in 59 packages // allocVector used 12419 times in 551 packages Rboolean Rf_conformable(SEXP, SEXP); // Rf_conformable unused // conformable used 141 times in 22 packages SEXP Rf_elt(SEXP, int); // Rf_elt unused // elt used 2310 times in 37 packages Rboolean Rf_inherits(SEXP, const char *); // Rf_inherits used 530 times in 21 packages // inherits used 814 times in 80 packages Rboolean Rf_isArray(SEXP); // Rf_isArray unused // isArray used 34 times in checkmate, PythonInR, data.table, ifultools, Rblpapi, Rvcg, unfoldr, TMB, kza, qtbase Rboolean Rf_isFactor(SEXP); // Rf_isFactor used 22 times in 11 packages // isFactor used 42 times in checkmate, rggobi, PythonInR, data.table, Kmisc, partykit, cba, qtbase, RSQLite Rboolean Rf_isFrame(SEXP); // Rf_isFrame used 1 times in OpenMx // isFrame used 15 times in checkmate, splusTimeDate, OjaNP, PythonInR, data.table, robfilter Rboolean Rf_isFunction(SEXP); // Rf_isFunction used 4 times in Rserve, genie, RcppClassic // isFunction used 274 times in 43 packages Rboolean Rf_isInteger(SEXP); // Rf_isInteger used 39 times in 14 packages // isInteger used 402 times in 77 packages Rboolean Rf_isLanguage(SEXP); // Rf_isLanguage unused // isLanguage used 63 times in PythonInR, rgp, RandomFields Rboolean Rf_isList(SEXP); // Rf_isList unused // isList used 40 times in 11 packages Rboolean Rf_isMatrix(SEXP); // Rf_isMatrix used 55 times in 16 packages // isMatrix used 293 times in 65 packages Rboolean Rf_isNewList(SEXP); // Rf_isNewList used 6 times in Rmosek, RcppClassic // isNewList used 103 times in 27 packages Rboolean Rf_isNumber(SEXP); // Rf_isNumber unused // isNumber used 14 times in PythonInR, readr, stringi, qtbase Rboolean Rf_isNumeric(SEXP); // Rf_isNumeric used 31 times in Rmosek, gaselect, RcppCNPy, genie, mets, Morpho, rstan, Rcpp, RcppClassic, OpenMx // isNumeric used 468 times in 49 packages Rboolean Rf_isPairList(SEXP); // Rf_isPairList unused // isPairList used 2 times in PythonInR Rboolean Rf_isPrimitive(SEXP); // Rf_isPrimitive unused // isPrimitive used 7 times in PythonInR, qtbase Rboolean Rf_isTs(SEXP); // Rf_isTs unused // isTs used 2 times in PythonInR Rboolean Rf_isUserBinop(SEXP); // Rf_isUserBinop unused // isUserBinop used 2 times in PythonInR Rboolean Rf_isValidString(SEXP); // Rf_isValidString unused // isValidString used 26 times in SSN, PythonInR, foreign, pbdMPI, RJSONIO, SASxport Rboolean Rf_isValidStringF(SEXP); // Rf_isValidStringF unused // isValidStringF used 2 times in PythonInR Rboolean Rf_isVector(SEXP); // Rf_isVector used 15 times in RProtoBuf, RcppCNPy, stringi, purrr, RcppClassic, OpenMx, adaptivetau // isVector used 182 times in 46 packages Rboolean Rf_isVectorAtomic(SEXP); // Rf_isVectorAtomic used 13 times in agop, tidyr, reshape2, stringi // isVectorAtomic used 40 times in bit, matrixStats, checkmate, PythonInR, data.table, Matrix, bit64, potts, aster2, qtbase Rboolean Rf_isVectorList(SEXP); // Rf_isVectorList used 23 times in genie, purrr, RNiftyReg, stringi // isVectorList used 12 times in RPostgreSQL, spsurvey, PythonInR, stringi, adaptivetau, PCICt, RandomFields Rboolean Rf_isVectorizable(SEXP); // Rf_isVectorizable unused // isVectorizable used 3 times in PythonInR, robfilter SEXP Rf_lang1(SEXP); // Rf_lang1 used 14 times in PopGenome, WhopGenome, nontarget, Rcpp11, purrr, Rcpp, CEGO // lang1 used 30 times in 11 packages SEXP Rf_lang2(SEXP, SEXP); // Rf_lang2 used 64 times in 13 packages // lang2 used 216 times in 75 packages SEXP Rf_lang3(SEXP, SEXP, SEXP); // Rf_lang3 used 19 times in purrr, RcppDE, Rcpp, lbfgs, emdist, Rcpp11 // lang3 used 107 times in 28 packages SEXP Rf_lang4(SEXP, SEXP, SEXP, SEXP); // Rf_lang4 used 8 times in lme4, purrr, Rcpp, diversitree, Rcpp11 // lang4 used 65 times in 21 packages SEXP Rf_lang5(SEXP, SEXP, SEXP, SEXP, SEXP); // Rf_lang5 unused // lang5 used 11 times in PBSddesolve, GNE, SMC SEXP Rf_lang6(SEXP, SEXP, SEXP, SEXP, SEXP, SEXP); // Rf_lang6 used 1 times in lme4 // lang6 used 2 times in GNE SEXP Rf_lastElt(SEXP); // Rf_lastElt unused // lastElt unused SEXP Rf_lcons(SEXP, SEXP); // Rf_lcons used 26 times in purrr, rcppbugs, Rcpp, pryr // lcons used 16 times in rmgarch R_len_t Rf_length(SEXP); // Rf_length used 662 times in 69 packages SEXP Rf_list1(SEXP); // Rf_list1 used 1 times in Rcpp // list1 used 197 times in 11 packages SEXP Rf_list2(SEXP, SEXP); // Rf_list2 unused // list2 used 441 times in 12 packages SEXP Rf_list3(SEXP, SEXP, SEXP); // Rf_list3 unused // list3 used 72 times in marked, Rdsdp, BH, svd SEXP Rf_list4(SEXP, SEXP, SEXP, SEXP); // Rf_list4 unused // list4 used 58 times in igraph, PBSddesolve, Rserve, BH, yaml, treethresh, SMC SEXP Rf_list5(SEXP, SEXP, SEXP, SEXP, SEXP); // Rf_list5 unused // list5 used 63 times in Rdsdp, BH SEXP Rf_listAppend(SEXP, SEXP); // Rf_listAppend unused // listAppend used 1 times in ore SEXP Rf_mkNamed(SEXPTYPE, const char **); // Rf_mkNamed used 8 times in Matrix, gmp, RSclient, HiPLARM // mkNamed used 12 times in RCassandra, coxme, SamplerCompare, survival, JavaGD, DEoptim, qtbase SEXP Rf_mkString(const char *); // Rf_mkString used 179 times in 24 packages // mkString used 814 times in 96 packages int Rf_nlevels(SEXP); // Rf_nlevels unused // nlevels used 546 times in 26 packages int Rf_stringPositionTr(SEXP, const char *); // Rf_stringPositionTr unused // stringPositionTr unused SEXP Rf_ScalarComplex(Rcomplex); // Rf_ScalarComplex unused // ScalarComplex unused SEXP Rf_ScalarInteger(int); // Rf_ScalarInteger used 390 times in 20 packages // ScalarInteger used 704 times in 88 packages SEXP Rf_ScalarLogical(int); // Rf_ScalarLogical used 160 times in 20 packages // ScalarLogical used 450 times in 64 packages SEXP Rf_ScalarRaw(Rbyte); // Rf_ScalarRaw unused // ScalarRaw used 4 times in qtbase, RGtk2 SEXP Rf_ScalarReal(double); // Rf_ScalarReal used 146 times in 19 packages // ScalarReal used 330 times in 65 packages SEXP Rf_ScalarString(SEXP); // Rf_ScalarString used 33 times in agop, Nippon, Rcpp11, rpf, stringi, purrr, Rcpp // ScalarString used 198 times in 37 packages R_xlen_t Rf_xlength(SEXP); // Rf_xlength used 46 times in WGCNA, Rcpp, Rcpp11 SEXP Rf_protect(SEXP); // Rf_protect used 332 times in 12 packages // protect used 599 times in 101 packages void Rf_unprotect(int); // Rf_unprotect used 289 times in 12 packages // unprotect used 110 times in 35 packages void R_ProtectWithIndex(SEXP, PROTECT_INDEX *); // R_ProtectWithIndex used 8 times in OpenMx void R_Reprotect(SEXP, PROTECT_INDEX); // R_Reprotect used 2 times in OpenMx SEXP R_FixupRHS(SEXP x, SEXP y); // R_FixupRHS unused } </pre> == Stats == <pre> 0 1 2 3 4 5 6 7 8 9 10+ Macro: 57 9 7 3 4 1 2 3 0 1 98 (usage count) (185) 57 20 8 2 6 4 4 5 3 4 72 (distinct package count) Function: 103 14 17 11 14 11 7 6 4 5 186 (usage count) (378) 103 30 30 20 28 7 9 6 1 5 139 (distinct package count) Variable: 26 0 4 2 1 0 1 0 0 0 20 (usage count) (54) 26 4 2 2 0 0 0 1 0 1 18 (distinct package count) TypeDef: 1 0 0 0 0 0 0 1 0 0 5 (usage count) (7) 1 0 0 1 0 0 0 0 0 0 5 (distinct package count) Alias: 43 9 9 8 6 4 0 3 1 1 97 (usage count) (181) 43 25 14 5 9 1 5 5 3 1 70 (distinct package count) </pre> (for a quick explanation of these stats see [[Native_API_stats_of_R.h]]) e4124b1f746332818e5ceb813403dc39c8728e9d 0 8 16 2016-06-20T15:20:08Z Lukasstadler 8 Created page with "== Input == <pre> #include "Rinternals.h" </pre> == Result == <pre> #define ANYSXP 18 // ANYSXP used 14 times in RPostgreSQL, Rcpp1..." wikitext text/x-wiki == Input == <pre> #include "Rinternals.h" </pre> == Result == <pre> #define ANYSXP 18 // ANYSXP used 14 times in RPostgreSQL, Rcpp11, seqminer, Rcpp, pryr, rtkpp, rtkore, RGtk2 #define BCODESXP 21 // BCODESXP used 15 times in rcppbugs, Rcpp11, seqminer, Rcpp, pryr, rtkpp, rtkore #define BCODE_CODE(x) CAR(x) // BCODE_CODE unused #define BCODE_CONSTS(x) CDR(x) // BCODE_CONSTS unused #define BCODE_EXPR(x) TAG(x) // BCODE_EXPR unused #define BODY_EXPR(e) R_ClosureExpr(e) // BODY_EXPR unused #define BUILTINSXP 8 // BUILTINSXP used 24 times in 11 packages #define CHAR(x) R_CHAR(x) // CHAR used 4405 times in 362 packages #define CHARSXP 9 // CHARSXP used 106 times in 33 packages #define CLOSXP 3 // CLOSXP used 83 times in 30 packages #define CONS(a, b) Rf_cons((a), (b)) // CONS used 458 times in 30 packages #define CPLXSXP 15 // CPLXSXP used 409 times in 49 packages #define CreateTag Rf_CreateTag // CreateTag used 1 times in rgp #define DECREMENT_REFCNT(x) do {} while(0) // DECREMENT_REFCNT unused #define DISABLE_REFCNT(x) do {} while(0) // DISABLE_REFCNT unused #define DOTSXP 17 // DOTSXP used 16 times in RPostgreSQL, PythonInR, Rcpp11, seqminer, Rcpp, pryr, rtkpp, spikeSlabGAM, rtkore #define DropDims Rf_DropDims // DropDims unused #define ENABLE_NLS 1 // ENABLE_NLS used 80 times in 59 packages #define ENABLE_REFCNT(x) do {} while(0) // ENABLE_REFCNT unused #define ENVSXP 4 // ENVSXP used 63 times in 25 packages #define EXPRSXP 20 // EXPRSXP used 84 times in 14 packages #define EXTPTRSXP 22 // EXTPTRSXP used 386 times in 55 packages #define EXTPTR_PROT(x) CDR(x) // EXTPTR_PROT used 5 times in rJava, pryr #define EXTPTR_PTR(x) CAR(x) // EXTPTR_PTR used 428 times in 15 packages #define EXTPTR_TAG(x) TAG(x) // EXTPTR_TAG used 9 times in excel.link, pryr, rJava, gsl #define FREESXP 31 // FREESXP used 4 times in rtkpp, rtkore #define FUNSXP 99 // FUNSXP used 6 times in dplyr, rtkpp, data.table, rtkore #define GetArrayDimnames Rf_GetArrayDimnames // GetArrayDimnames unused #define GetColNames Rf_GetColNames // GetColNames unused #define GetMatrixDimnames Rf_GetMatrixDimnames // GetMatrixDimnames used 2 times in Kmisc, optmatch #define GetOption Rf_GetOption // GetOption used 5 times in rgl, gmp, Cairo, RGtk2 #define GetOption1 Rf_GetOption1 // GetOption1 used 1 times in PCICt #define GetOptionDigits Rf_GetOptionDigits // GetOptionDigits unused #define GetOptionWidth Rf_GetOptionWidth // GetOptionWidth unused #define GetRowNames Rf_GetRowNames // GetRowNames unused #define HAVE_ALLOCA_H 1 // HAVE_ALLOCA_H used 15 times in treatSens, Matrix, TMB, pbdZMQ, ore, dbarts #define HAVE_AQUA 1 // HAVE_AQUA used 13 times in 11 packages #define HAVE_F77_UNDERSCORE 1 // HAVE_F77_UNDERSCORE used 2 times in igraph #define IEEE_754 1 // IEEE_754 used 47 times in igraph, Rcpp, data.table, stringi #define INCREMENT_NAMED(x) do { SEXP __x__ = (x); if (NAMED(__x__) != 2) SET_NAMED(__x__, NAMED(__x__) + 1); } while (0) // INCREMENT_NAMED unused #define INCREMENT_REFCNT(x) do {} while(0) // INCREMENT_REFCNT unused #define INLINE_PROTECT // INLINE_PROTECT unused #define INTSXP 13 // INTSXP used 6341 times in 471 packages #define ISNA(x) R_IsNA(x) // ISNA used 649 times in 100 packages #define ISNAN(x) R_isnancpp(x) // ISNAN used 1342 times in 146 packages #define IS_GETTER_CALL(call) (CADR(call) == R_TmpvalSymbol) // IS_GETTER_CALL unused #define IS_SCALAR(x, type) (TYPEOF(x) == (type) && XLENGTH(x) == 1) // IS_SCALAR unused #define IS_SIMPLE_SCALAR(x, type) ((TYPEOF(x) == (type) && XLENGTH(x) == 1) && ATTRIB(x) == R_NilValue) // IS_SIMPLE_SCALAR unused #define IndexWidth Rf_IndexWidth // IndexWidth unused #define LANGSXP 6 // LANGSXP used 1276 times in 53 packages #define LCONS(a, b) Rf_lcons((a), (b)) // LCONS used 212 times in 24 packages #define LGLSXP 10 // LGLSXP used 1430 times in 166 packages #define LISTSXP 2 // LISTSXP used 87 times in 21 packages #define LONG_VECTOR_SUPPORT // LONG_VECTOR_SUPPORT used 56 times in stringdist, matrixStats, RApiSerialize, Rhpc, pbdMPI, Rcpp11, Matrix #define LibExport // LibExport used 2 times in hsmm #define LibExtern extern // LibExtern used 4 times in rJava #define LibImport // LibImport unused #define MARK_NOT_MUTABLE(x) SET_NAMED(x, 2) // MARK_NOT_MUTABLE unused #define MAX_NUM_SEXPTYPE (1<<5) // MAX_NUM_SEXPTYPE unused #define MAYBE_REFERENCED(x) (! (NAMED(x) == 0)) // MAYBE_REFERENCED unused #define MAYBE_SHARED(x) (NAMED(x) > 1) // MAYBE_SHARED unused #define NAMEDMAX 2 // NAMEDMAX unused #define NA_INTEGER R_NaInt // NA_INTEGER used 1520 times in 183 packages #define NA_LOGICAL R_NaInt // NA_LOGICAL used 355 times in 73 packages #define NA_REAL R_NaReal // NA_REAL used 1667 times in 226 packages #define NA_STRING R_NaString // NA_STRING used 574 times in 90 packages #define NEWSXP 30 // NEWSXP used 4 times in rtkpp, rtkore #define NILSXP 0 // NILSXP used 169 times in 44 packages #define NORET __attribute__((noreturn)) // NORET unused #define NOT_SHARED(x) (! (NAMED(x) > 1)) // NOT_SHARED unused #define NO_REFERENCES(x) (NAMED(x) == 0) // NO_REFERENCES unused #define NonNullStringMatch Rf_NonNullStringMatch // NonNullStringMatch used 8 times in proxy, arules, arulesSequences, cba #define PREXPR(e) R_PromiseExpr(e) // PREXPR used 4 times in igraph, lazyeval #define PROMSXP 5 // PROMSXP used 43 times in 14 packages #define PROTECT(s) Rf_protect(s) // PROTECT used 24686 times in 767 packages #define PROTECT_WITH_INDEX(x,i) R_ProtectWithIndex(x,i) // PROTECT_WITH_INDEX used 91 times in 27 packages #define PairToVectorList Rf_PairToVectorList // PairToVectorList used 7 times in cba, rcdd #define PrintValue Rf_PrintValue // PrintValue used 119 times in 13 packages #define RAWSXP 24 // RAWSXP used 587 times in 92 packages #define REALSXP 14 // REALSXP used 10171 times in 573 packages #define REPROTECT(x,i) R_Reprotect(x,i) // REPROTECT used 130 times in 25 packages #define R_ALLOCATOR_TYPE // R_ALLOCATOR_TYPE unused #define R_ARITH_H_ // R_ARITH_H_ unused #define R_COMPLEX_H // R_COMPLEX_H used 1 times in uniqueAtomMat #define R_ERROR_H_ // R_ERROR_H_ unused #define R_EXT_BOOLEAN_H_ // R_EXT_BOOLEAN_H_ used 2 times in jpeg, Rcpp11 #define R_EXT_MEMORY_H_ // R_EXT_MEMORY_H_ unused #define R_EXT_PRINT_H_ // R_EXT_PRINT_H_ used 6 times in spTDyn, spTimer #define R_EXT_UTILS_H_ // R_EXT_UTILS_H_ unused #define R_FINITE(x) R_finite(x) // R_FINITE used 1387 times in 145 packages #define R_INLINE inline // R_INLINE used 330 times in 34 packages #define R_INTERNALS_H_ // R_INTERNALS_H_ used 7 times in uniqueAtomMat, rtkpp, rtkore, spatstat #define R_LEN_T_MAX 2147483647 // R_LEN_T_MAX used 4 times in stringdist, matrixStats, FREGAT, Rcpp11 #define R_LONG_VEC_TOKEN -1 // R_LONG_VEC_TOKEN used 1 times in Rcpp11 #define R_RCONFIG_H // R_RCONFIG_H unused #define R_SHORT_LEN_MAX 2147483647 // R_SHORT_LEN_MAX used 1 times in pbdMPI #define R_XDR_DOUBLE_SIZE 8 // R_XDR_DOUBLE_SIZE used 2 times in rgdal #define R_XDR_INTEGER_SIZE 4 // R_XDR_INTEGER_SIZE used 3 times in rgdal #define R_XLEN_T_MAX 4503599627370496 // R_XLEN_T_MAX used 7 times in stringdist, Matrix, matrixStats, RApiSerialize, Rhpc #define S3Class Rf_S3Class // S3Class used 4 times in RInside, littler #define S4SXP 25 // S4SXP used 71 times in 15 packages #define SET_REFCNT(x,v) do {} while(0) // SET_REFCNT unused #define SET_TRACKREFS(x,v) do {} while(0) // SET_TRACKREFS unused #define SIZEOF_SIZE_T 8 // SIZEOF_SIZE_T used 1 times in PythonInR #define SPECIALSXP 7 // SPECIALSXP used 22 times in RPostgreSQL, PythonInR, Rcpp11, purrr, seqminer, Rcpp, yaml, pryr, rtkpp, rtkore #define STRSXP 16 // STRSXP used 3247 times in 327 packages #define SUPPORT_MBCS 1 // SUPPORT_MBCS used 1 times in bibtex #define SUPPORT_UTF8 1 // SUPPORT_UTF8 used 3 times in tau, rindex, stringi #define SYMSXP 1 // SYMSXP used 94 times in 25 packages #define ScalarComplex Rf_ScalarComplex // ScalarComplex unused #define ScalarInteger Rf_ScalarInteger // ScalarInteger used 704 times in 88 packages #define ScalarLogical Rf_ScalarLogical // ScalarLogical used 450 times in 64 packages #define ScalarRaw Rf_ScalarRaw // ScalarRaw used 4 times in qtbase, RGtk2 #define ScalarReal Rf_ScalarReal // ScalarReal used 330 times in 65 packages #define ScalarString Rf_ScalarString // ScalarString used 198 times in 37 packages #define StringBlank Rf_StringBlank // StringBlank unused #define StringFalse Rf_StringFalse // StringFalse used 3 times in iotools #define StringTrue Rf_StringTrue // StringTrue used 3 times in iotools #define TYPE_BITS 5 // TYPE_BITS used 2 times in dplyr #define UNPROTECT(n) Rf_unprotect(n) // UNPROTECT used 12247 times in 758 packages #define UNPROTECT_PTR(s) Rf_unprotect_ptr(s) // UNPROTECT_PTR used 307 times in 14 packages #define VECSXP 19 // VECSXP used 3142 times in 385 packages #define VectorToPairList Rf_VectorToPairList // VectorToPairList used 13 times in pomp, arules #define WEAKREFSXP 23 // WEAKREFSXP used 19 times in seqminer, Rcpp, pryr, rtkpp, rtkore, Rcpp11 #define acopy_string Rf_acopy_string // acopy_string used 10 times in splusTimeDate #define addMissingVarsToNewEnv Rf_addMissingVarsToNewEnv // addMissingVarsToNewEnv unused #define alloc3DArray Rf_alloc3DArray // alloc3DArray used 21 times in mcmc, msm, TPmsm, unfoldr, RandomFields, cplm #define allocArray Rf_allocArray // allocArray used 24 times in unfoldr, kergp, pomp, proxy, kza, slam, mvMORPH, TPmsm, ouch, RandomFields #define allocFormalsList2 Rf_allocFormalsList2 // allocFormalsList2 unused #define allocFormalsList3 Rf_allocFormalsList3 // allocFormalsList3 unused #define allocFormalsList4 Rf_allocFormalsList4 // allocFormalsList4 unused #define allocFormalsList5 Rf_allocFormalsList5 // allocFormalsList5 unused #define allocFormalsList6 Rf_allocFormalsList6 // allocFormalsList6 unused #define allocList Rf_allocList // allocList used 60 times in 25 packages #define allocMatrix Rf_allocMatrix // allocMatrix used 1577 times in 244 packages #define allocS4Object Rf_allocS4Object // allocS4Object used 1 times in arules #define allocSExp Rf_allocSExp // allocSExp used 14 times in igraph, rgp, data.table, RandomFields, mmap, qtbase #define allocVector Rf_allocVector // allocVector used 12419 times in 551 packages #define allocVector3 Rf_allocVector3 // allocVector3 unused #define any_duplicated Rf_any_duplicated // any_duplicated used 5 times in data.table, checkmate #define any_duplicated3 Rf_any_duplicated3 // any_duplicated3 unused #define applyClosure Rf_applyClosure // applyClosure unused #define arraySubscript Rf_arraySubscript // arraySubscript used 13 times in proxy, arules, arulesSequences, cba, seriation #define asChar Rf_asChar // asChar used 194 times in 36 packages #define asCharacterFactor Rf_asCharacterFactor // asCharacterFactor used 11 times in fastmatch, Kmisc, data.table #define asComplex Rf_asComplex // asComplex used 1 times in ff #define asInteger Rf_asInteger // asInteger used 1277 times in 140 packages #define asLogical Rf_asLogical // asLogical used 462 times in 64 packages #define asReal Rf_asReal // asReal used 383 times in 83 packages #define asS4 Rf_asS4 // asS4 unused #define cPsort Rf_cPsort // cPsort unused #define classgets Rf_classgets // classgets used 91 times in 30 packages #define coerceVector Rf_coerceVector // coerceVector used 2585 times in 167 packages #define conformable Rf_conformable // conformable used 141 times in 22 packages #define cons Rf_cons // cons used 609 times in 39 packages #define copyListMatrix Rf_copyListMatrix // copyListMatrix used 1 times in Matrix #define copyMatrix Rf_copyMatrix // copyMatrix used 7 times in BDgraph, Matrix, kza #define copyMostAttrib Rf_copyMostAttrib // copyMostAttrib used 68 times in arules, robustbase, data.table, xts, memisc, proxy, zoo, tau #define copyVector Rf_copyVector // copyVector used 12 times in tm, kza, mlegp, adaptivetau #define countContexts Rf_countContexts // countContexts unused #define defineVar Rf_defineVar // defineVar used 218 times in 38 packages #define dimgets Rf_dimgets // dimgets used 3 times in CorrBin #define dimnamesgets Rf_dimnamesgets // dimnamesgets used 24 times in Matrix, RxCEcolInf, lxb, sapa #define duplicate Rf_duplicate // duplicate used 2088 times in 224 packages #define duplicated Rf_duplicated // duplicated used 402 times in 100 packages #define elt Rf_elt // elt used 2310 times in 37 packages #define error Rf_error // error used 63771 times in 1109 packages #define error_return(msg) { Rf_error(msg); return R_NilValue; } // error_return used 100 times in rpg, RPostgreSQL, Rook, git2r, grr, rJava, rmumps #define errorcall Rf_errorcall // errorcall used 103 times in RCurl, arules, XML, arulesSequences, pbdMPI, xts, proxy, cba, rJava, RSAP #define errorcall_return(cl,msg) { Rf_errorcall(cl, msg); return R_NilValue; } // errorcall_return used 31 times in Runuran #define eval Rf_eval // eval used 25178 times in 269 packages #define findFun Rf_findFun // findFun used 13 times in sprint, tikzDevice, yaml, unfoldr, TraMineR, RGtk2 #define findVar Rf_findVar // findVar used 1333 times in 24 packages #define findVarInFrame Rf_findVarInFrame // findVarInFrame used 101 times in 13 packages #define findVarInFrame3 Rf_findVarInFrame3 // findVarInFrame3 used 5 times in datamap #define getAttrib Rf_getAttrib // getAttrib used 1930 times in 239 packages #define getCharCE Rf_getCharCE // getCharCE used 16 times in ore, RSclient, PythonInR, Rserve, jsonlite, tau, rJava #define gsetVar Rf_gsetVar // gsetVar used 4 times in RSVGTipsDevice, Cairo, RSvgDevice, JavaGD #define iPsort Rf_iPsort // iPsort used 3 times in matrixStats, robustbase #define inherits Rf_inherits // inherits used 814 times in 80 packages #define install Rf_install // install used 3178 times in 224 packages #define installChar Rf_installChar // installChar used 4 times in dplyr #define installDDVAL Rf_installDDVAL // installDDVAL unused #define installS3Signature Rf_installS3Signature // installS3Signature unused #define isArray Rf_isArray // isArray used 34 times in checkmate, PythonInR, data.table, ifultools, Rblpapi, Rvcg, unfoldr, TMB, kza, qtbase #define isBasicClass Rf_isBasicClass // isBasicClass unused #define isBlankString Rf_isBlankString // isBlankString used 1 times in iotools #define isByteCode(x) (TYPEOF(x)==21) // isByteCode unused #define isComplex(s) Rf_isComplex(s) // isComplex used 119 times in checkmate, PythonInR, ifultools, Rblpapi, Rcpp11, rmatio, stringi, Matrix, qtbase #define isEnvironment(s) Rf_isEnvironment(s) // isEnvironment used 113 times in 52 packages #define isExpression(s) Rf_isExpression(s) // isExpression used 3 times in PythonInR, Rcpp11 #define isFactor Rf_isFactor // isFactor used 42 times in checkmate, rggobi, PythonInR, data.table, Kmisc, partykit, cba, qtbase, RSQLite #define isFrame Rf_isFrame // isFrame used 15 times in checkmate, splusTimeDate, OjaNP, PythonInR, data.table, robfilter #define isFree Rf_isFree // isFree unused #define isFunction Rf_isFunction // isFunction used 274 times in 43 packages #define isInteger Rf_isInteger // isInteger used 402 times in 77 packages #define isLanguage Rf_isLanguage // isLanguage used 63 times in PythonInR, rgp, RandomFields #define isList Rf_isList // isList used 40 times in 11 packages #define isLogical(s) Rf_isLogical(s) // isLogical used 215 times in 53 packages #define isMatrix Rf_isMatrix // isMatrix used 293 times in 65 packages #define isNewList Rf_isNewList // isNewList used 103 times in 27 packages #define isNull(s) Rf_isNull(s) // isNull used 1915 times in 119 packages #define isNumber Rf_isNumber // isNumber used 14 times in PythonInR, readr, stringi, qtbase #define isNumeric Rf_isNumeric // isNumeric used 468 times in 49 packages #define isObject(s) Rf_isObject(s) // isObject used 11 times in dplyr, Rcpp, PythonInR, Rcpp11, stringi, rmumps #define isOrdered Rf_isOrdered // isOrdered used 65 times in partykit, PythonInR, data.table, RSQLite #define isPairList Rf_isPairList // isPairList used 2 times in PythonInR #define isPrimitive Rf_isPrimitive // isPrimitive used 7 times in PythonInR, qtbase #define isReal(s) Rf_isReal(s) // isReal used 323 times in 64 packages #define isS4 Rf_isS4 // isS4 used 13 times in PythonInR, Rcpp11, dplyr, Rcpp, catnet, rmumps, sdnet #define isString(s) Rf_isString(s) // isString used 280 times in 59 packages #define isSymbol(s) Rf_isSymbol(s) // isSymbol used 68 times in PythonInR, data.table, Rcpp11, stringi, rgp, dbarts, rJava, sourcetools #define isTs Rf_isTs // isTs used 2 times in PythonInR #define isUnordered Rf_isUnordered // isUnordered used 2 times in PythonInR #define isUnsorted Rf_isUnsorted // isUnsorted unused #define isUserBinop Rf_isUserBinop // isUserBinop used 2 times in PythonInR #define isValidString Rf_isValidString // isValidString used 26 times in SSN, PythonInR, foreign, pbdMPI, RJSONIO, SASxport #define isValidStringF Rf_isValidStringF // isValidStringF used 2 times in PythonInR #define isVector Rf_isVector // isVector used 182 times in 46 packages #define isVectorAtomic Rf_isVectorAtomic // isVectorAtomic used 40 times in bit, matrixStats, checkmate, PythonInR, data.table, Matrix, bit64, potts, aster2, qtbase #define isVectorList Rf_isVectorList // isVectorList used 12 times in RPostgreSQL, spsurvey, PythonInR, stringi, adaptivetau, PCICt, RandomFields #define isVectorizable Rf_isVectorizable // isVectorizable used 3 times in PythonInR, robfilter #define lang1 Rf_lang1 // lang1 used 30 times in 11 packages #define lang2 Rf_lang2 // lang2 used 216 times in 75 packages #define lang3 Rf_lang3 // lang3 used 107 times in 28 packages #define lang4 Rf_lang4 // lang4 used 65 times in 21 packages #define lang5 Rf_lang5 // lang5 used 11 times in PBSddesolve, GNE, SMC #define lang6 Rf_lang6 // lang6 used 2 times in GNE #define lastElt Rf_lastElt // lastElt unused #define lazy_duplicate Rf_lazy_duplicate // lazy_duplicate unused #define lcons Rf_lcons // lcons used 16 times in rmgarch #define length(x) Rf_length(x) // length used 44060 times in 1224 packages #define lengthgets Rf_lengthgets // lengthgets used 47 times in 11 packages #define list1 Rf_list1 // list1 used 197 times in 11 packages #define list2 Rf_list2 // list2 used 441 times in 12 packages #define list3 Rf_list3 // list3 used 72 times in marked, Rdsdp, BH, svd #define list4 Rf_list4 // list4 used 58 times in igraph, PBSddesolve, Rserve, BH, yaml, treethresh, SMC #define list5 Rf_list5 // list5 used 63 times in Rdsdp, BH #define listAppend Rf_listAppend // listAppend used 1 times in ore #define match Rf_match // match used 8773 times in 388 packages #define matchE Rf_matchE // matchE unused #define mkChar Rf_mkChar // mkChar used 4545 times in 287 packages #define mkCharCE Rf_mkCharCE // mkCharCE used 72 times in 15 packages #define mkCharLen Rf_mkCharLen // mkCharLen used 38 times in 16 packages #define mkCharLenCE Rf_mkCharLenCE // mkCharLenCE used 23 times in 11 packages #define mkNamed Rf_mkNamed // mkNamed used 12 times in RCassandra, coxme, SamplerCompare, survival, JavaGD, DEoptim, qtbase #define mkString Rf_mkString // mkString used 814 times in 96 packages #define namesgets Rf_namesgets // namesgets used 80 times in 14 packages #define ncols Rf_ncols // ncols used 3805 times in 182 packages #define nlevels Rf_nlevels // nlevels used 546 times in 26 packages #define nrows Rf_nrows // nrows used 4332 times in 215 packages #define nthcdr Rf_nthcdr // nthcdr used 9 times in sprint, rmongodb, PythonInR, xts #define pmatch Rf_pmatch // pmatch used 169 times in ore, git2r, AdaptFitOS, data.table, seqminer, locfit, oce, rmumps #define protect Rf_protect // protect used 599 times in 101 packages #define psmatch Rf_psmatch // psmatch used 5 times in rgl #define rPsort Rf_rPsort // rPsort used 63 times in 15 packages #define reEnc Rf_reEnc // reEnc used 3 times in PythonInR, RJSONIO #define readS3VarsFromFrame Rf_readS3VarsFromFrame // readS3VarsFromFrame unused #define revsort Rf_revsort // revsort used 60 times in 20 packages #define rownamesgets Rf_rownamesgets // rownamesgets unused #define setAttrib Rf_setAttrib // setAttrib used 1830 times in 251 packages #define setIVector Rf_setIVector // setIVector unused #define setRVector Rf_setRVector // setRVector used 3 times in RcppClassic, RcppClassicExamples #define setSVector Rf_setSVector // setSVector unused #define setVar Rf_setVar // setVar used 24 times in Rhpc, rscproxy, PythonInR, rgenoud, survival, gsl, littler, spatstat #define shallow_duplicate Rf_shallow_duplicate // shallow_duplicate used 2 times in tmlenet, smint #define str2type Rf_str2type // str2type used 1 times in RGtk2 #define stringPositionTr Rf_stringPositionTr // stringPositionTr unused #define stringSuffix Rf_stringSuffix // stringSuffix unused #define substitute Rf_substitute // substitute used 255 times in 56 packages #define topenv Rf_topenv // topenv unused #define translateChar Rf_translateChar // translateChar used 59 times in 19 packages #define translateChar0 Rf_translateChar0 // translateChar0 unused #define translateCharUTF8 Rf_translateCharUTF8 // translateCharUTF8 used 66 times in 13 packages #define type2char Rf_type2char // type2char used 107 times in 12 packages #define type2rstr Rf_type2rstr // type2rstr unused #define type2str Rf_type2str // type2str used 3 times in Kmisc, yaml #define type2str_nowarn Rf_type2str_nowarn // type2str_nowarn used 1 times in qrmtools #define unprotect Rf_unprotect // unprotect used 110 times in 35 packages #define unprotect_ptr Rf_unprotect_ptr // unprotect_ptr unused #define warning Rf_warning // warning used 7679 times in 434 packages #define warningcall Rf_warningcall // warningcall used 4 times in RInside, jsonlite, pbdMPI #define warningcall_immediate Rf_warningcall_immediate // warningcall_immediate used 2 times in Runuran #define xlength(x) Rf_xlength(x) // xlength used 186 times in stringdist, yuima, matrixStats, Rhpc, validate, checkmate, dplR, Rdsdp, pscl, DescTools #define xlengthgets Rf_xlengthgets // xlengthgets unused # /Users/ls/Source/git/fastr/com.oracle.truffle.r.native/include/R_ext/Arith.h extern "C" { extern double R_NaN; // R_NaN used 469 times in 68 packages extern double R_PosInf; // R_PosInf used 562 times in 112 packages extern double R_NegInf; // R_NegInf used 699 times in 105 packages extern double R_NaReal; // R_NaReal used 140 times in 34 packages // NA_REAL used 1667 times in 226 packages extern int R_NaInt; // R_NaInt used 58 times in 20 packages // NA_INTEGER used 1520 times in 183 packages // NA_LOGICAL used 355 times in 73 packages int R_IsNA(double); // R_IsNA used 161 times in 40 packages int R_IsNaN(double); // R_IsNaN used 75 times in 28 packages int R_finite(double); // R_finite used 232 times in 44 packages int R_isnancpp(double); // R_isnancpp used 8 times in igraph, PwrGSD } # /Users/ls/Source/git/fastr/com.oracle.truffle.r.native/include/R_ext/Boolean.h extern "C" { typedef enum { FALSE = 0, TRUE } Rboolean; } # /Users/ls/Source/git/fastr/com.oracle.truffle.r.native/include/R_ext/Complex.h extern "C" { typedef struct { double r; double i; } Rcomplex; // Rcomplex used 893 times in 47 packages } # /Users/ls/Source/git/fastr/com.oracle.truffle.r.native/include/R_ext/Error.h extern "C" { void __attribute__((noreturn)) Rf_error(const char *, ...); void __attribute__((noreturn)) UNIMPLEMENTED(const char *); void __attribute__((noreturn)) WrongArgCount(const char *); void Rf_warning(const char *, ...); // Rf_warning used 316 times in 66 packages // warning used 7679 times in 434 packages void R_ShowMessage(const char *s); // R_ShowMessage used 104 times in Rserve, rJava, HiPLARM } # /Users/ls/Source/git/fastr/com.oracle.truffle.r.native/include/R_ext/Memory.h extern "C" { void* vmaxget(void); // vmaxget used 279 times in 20 packages void vmaxset(const void *); // vmaxset used 279 times in 20 packages void R_gc(void); // R_gc used 6 times in TMB, excel.link, gmatrix, microbenchmark int R_gc_running(); // R_gc_running unused char* R_alloc(size_t, int); // R_alloc used 7787 times in 330 packages long double *R_allocLD(size_t nelem); char* S_alloc(long, int); // S_alloc used 540 times in 50 packages char* S_realloc(char *, long, long, int); // S_realloc used 55 times in 11 packages } # /Users/ls/Source/git/fastr/com.oracle.truffle.r.native/include/R_ext/Print.h extern "C" { void Rprintf(const char *, ...); // Rprintf used 33813 times in 729 packages void REprintf(const char *, ...); // REprintf used 2531 times in 135 packages } # /Users/ls/Source/git/fastr/com.oracle.truffle.r.native/include/R_ext/Utils.h extern "C" { void R_isort(int*, int); // R_isort used 45 times in 18 packages void R_rsort(double*, int); // R_rsort used 210 times in 29 packages void R_csort(Rcomplex*, int); // R_csort unused void rsort_with_index(double *, int *, int); // rsort_with_index used 77 times in 40 packages void Rf_revsort(double*, int*, int); // Rf_revsort unused // revsort used 60 times in 20 packages void Rf_iPsort(int*, int, int); // Rf_iPsort unused // iPsort used 3 times in matrixStats, robustbase void Rf_rPsort(double*, int, int); // Rf_rPsort unused // rPsort used 63 times in 15 packages void Rf_cPsort(Rcomplex*, int, int); // Rf_cPsort unused // cPsort unused void R_qsort (double *v, size_t i, size_t j); // R_qsort used 10 times in extWeibQuant, pomp, robustbase, dplR, tclust, pcaPP void R_qsort_I (double *v, int *II, int i, int j); // R_qsort_I used 33 times in 15 packages void R_qsort_int (int *iv, size_t i, size_t j); // R_qsort_int unused void R_qsort_int_I(int *iv, int *II, int i, int j); // R_qsort_int_I used 19 times in ff, matrixStats, arules, Rborist, slam, eco, bnlearn const char *R_ExpandFileName(const char *); // R_ExpandFileName used 42 times in 20 packages void Rf_setIVector(int*, int, int); // Rf_setIVector unused // setIVector unused void Rf_setRVector(double*, int, double); // Rf_setRVector unused // setRVector used 3 times in RcppClassic, RcppClassicExamples Rboolean Rf_StringFalse(const char *); // Rf_StringFalse unused // StringFalse used 3 times in iotools Rboolean Rf_StringTrue(const char *); // Rf_StringTrue unused // StringTrue used 3 times in iotools Rboolean Rf_isBlankString(const char *); // Rf_isBlankString unused // isBlankString used 1 times in iotools double R_atof(const char *str); // R_atof used 9 times in SSN, tree, foreign, iotools double R_strtod(const char *c, char **end); // R_strtod used 4 times in ape, iotools char *R_tmpnam(const char *prefix, const char *tempdir); // R_tmpnam used 2 times in geometry char *R_tmpnam2(const char *prefix, const char *tempdir, const char *fileext); // R_tmpnam2 unused void R_CheckUserInterrupt(void); // R_CheckUserInterrupt used 1487 times in 234 packages void R_CheckStack(void); // R_CheckStack used 115 times in vcrpart, actuar, cplm, lme4, Matrix, GNE, randtoolbox, HiPLARM, rngWELL, pedigreemm void R_CheckStack2(size_t); // R_CheckStack2 unused int findInterval(double *xt, int n, double x, // findInterval used 11 times in BSquare, DNAprofiles, unfoldr, chebpol, pomp, eco, protViz, PBSmapping, spatstat Rboolean rightmost_closed, Rboolean all_inside, int ilo, int *mflag); void find_interv_vec(double *xt, int *n, double *x, int *nx, // find_interv_vec unused int *rightmost_closed, int *all_inside, int *indx); void R_max_col(double *matrix, int *nr, int *nc, int *maxes, int *ties_meth); // R_max_col used 2 times in geostatsp, MNP } # /Users/ls/Source/git/fastr/com.oracle.truffle.r.native/include/Rinternals.h extern "C" { typedef unsigned char Rbyte; typedef int R_len_t; // R_len_t used 2397 times in 70 packages typedef ptrdiff_t R_xlen_t; // R_xlen_t used 1537 times in 32 packages typedef struct { R_xlen_t lv_length, lv_truelength; } R_long_vec_hdr_t; typedef unsigned int SEXPTYPE; typedef struct SEXPREC *SEXP; const char *(R_CHAR)(SEXP x); // R_CHAR used 7 times in OpenMx, rjags, rpf Rboolean (Rf_isNull)(SEXP s); // Rf_isNull used 275 times in 43 packages Rboolean (Rf_isSymbol)(SEXP s); // Rf_isSymbol used 4 times in agop, PopGenome, WhopGenome, Rcpp11 Rboolean (Rf_isLogical)(SEXP s); // Rf_isLogical used 16 times in agop, Rmosek, Rcpp11, mets, seqminer, Rcpp, RcppClassic Rboolean (Rf_isReal)(SEXP s); // Rf_isReal used 26 times in agop, Rmosek, Rcpp11, rpf, genie, lme4, seqminer, RcppClassic, OpenMx Rboolean (Rf_isComplex)(SEXP s); // Rf_isComplex used 3 times in mets, Rcpp11 Rboolean (Rf_isExpression)(SEXP s); // Rf_isExpression used 1 times in Rcpp11 Rboolean (Rf_isEnvironment)(SEXP s); // Rf_isEnvironment used 5 times in Rcpp, Rcpp11 Rboolean (Rf_isString)(SEXP s); // Rf_isString used 39 times in 13 packages Rboolean (Rf_isObject)(SEXP s); // Rf_isObject used 4 times in genie, Rcpp, Rcpp11 SEXP (ATTRIB)(SEXP x); // ATTRIB used 83 times in 20 packages int (OBJECT)(SEXP x); // OBJECT used 102 times in 28 packages int (MARK)(SEXP x); // MARK used 251 times in 21 packages int (TYPEOF)(SEXP x); // TYPEOF used 2832 times in 195 packages int (NAMED)(SEXP x); // NAMED used 62 times in 22 packages int (REFCNT)(SEXP x); // REFCNT unused void (SET_OBJECT)(SEXP x, int v); // SET_OBJECT used 32 times in RSclient, reshape2, Rserve, data.table, actuar, dplyr, proxy, rmongodb, slam, tau void (SET_TYPEOF)(SEXP x, int v); // SET_TYPEOF used 38 times in 21 packages void (SET_NAMED)(SEXP x, int v); // SET_NAMED used 10 times in dplyr, yaml, data.table, iotools, RSQLite void SET_ATTRIB(SEXP x, SEXP v); // SET_ATTRIB used 54 times in 18 packages void DUPLICATE_ATTRIB(SEXP to, SEXP from); // DUPLICATE_ATTRIB used 5 times in covr, lfe, testthat, data.table int (IS_S4_OBJECT)(SEXP x); // IS_S4_OBJECT used 23 times in Rmosek, Runuran, data.table, xts, Matrix, slam, zoo, HiPLARM, OpenMx, tau void (SET_S4_OBJECT)(SEXP x); // SET_S4_OBJECT used 12 times in RSclient, redland, Rserve, data.table, FREGAT, rJPSGCS, tau void (UNSET_S4_OBJECT)(SEXP x); // UNSET_S4_OBJECT used 2 times in data.table, slam int (LENGTH)(SEXP x); // LENGTH used 5845 times in 356 packages int (TRUELENGTH)(SEXP x); // TRUELENGTH used 37 times in data.table void (SETLENGTH)(SEXP x, int v); // SETLENGTH used 65 times in 11 packages void (SET_TRUELENGTH)(SEXP x, int v); // SET_TRUELENGTH used 26 times in data.table R_xlen_t (XLENGTH)(SEXP x); // XLENGTH used 287 times in 21 packages R_xlen_t (XTRUELENGTH)(SEXP x); // XTRUELENGTH unused int (IS_LONG_VEC)(SEXP x); // IS_LONG_VEC used 1 times in RProtoBuf int (LEVELS)(SEXP x); // LEVELS used 18 times in rtdists, rPref, BsMD, data.table, stringi, dplyr, OBsMD, pbdZMQ, astrochron, RandomFields int (SETLEVELS)(SEXP x, int v); // SETLEVELS used 2 times in Rcpp11 int *(LOGICAL)(SEXP x); // LOGICAL used 4473 times in 288 packages int *(INTEGER)(SEXP x); // INTEGER used 41659 times in 758 packages Rbyte *(RAW)(SEXP x); // RAW used 880 times in 99 packages double *(REAL)(SEXP x); // REAL used 30947 times in 687 packages Rcomplex *(COMPLEX)(SEXP x); // COMPLEX used 1697 times in 71 packages SEXP (STRING_ELT)(SEXP x, R_xlen_t i); // STRING_ELT used 4143 times in 333 packages SEXP (VECTOR_ELT)(SEXP x, R_xlen_t i); // VECTOR_ELT used 8626 times in 291 packages void SET_STRING_ELT(SEXP x, R_xlen_t i, SEXP v); // SET_STRING_ELT used 5834 times in 321 packages SEXP SET_VECTOR_ELT(SEXP x, R_xlen_t i, SEXP v); // SET_VECTOR_ELT used 9751 times in 391 packages SEXP *(STRING_PTR)(SEXP x); // STRING_PTR used 65 times in 14 packages SEXP * __attribute__((noreturn)) (VECTOR_PTR)(SEXP x); SEXP (TAG)(SEXP e); // TAG used 513 times in 40 packages SEXP (CAR)(SEXP e); // CAR used 575 times in 63 packages SEXP (CDR)(SEXP e); // CDR used 4523 times in 76 packages SEXP (CAAR)(SEXP e); // CAAR unused SEXP (CDAR)(SEXP e); // CDAR unused SEXP (CADR)(SEXP e); // CADR used 104 times in 17 packages SEXP (CDDR)(SEXP e); // CDDR used 52 times in Rlabkey, Rcpp11, dplyr, proxy, Rcpp, slam, tikzDevice, OpenCL, svd SEXP (CDDDR)(SEXP e); // CDDDR unused SEXP (CADDR)(SEXP e); // CADDR used 52 times in 11 packages SEXP (CADDDR)(SEXP e); // CADDDR used 21 times in RPostgreSQL, foreign, actuar, bibtex SEXP (CAD4R)(SEXP e); // CAD4R used 14 times in earth, foreign, actuar int (MISSING)(SEXP x); // MISSING used 125 times in 25 packages void (SET_MISSING)(SEXP x, int v); // SET_MISSING used 1 times in sprint void SET_TAG(SEXP x, SEXP y); // SET_TAG used 200 times in 34 packages SEXP SETCAR(SEXP x, SEXP y); // SETCAR used 4072 times in 47 packages SEXP SETCDR(SEXP x, SEXP y); // SETCDR used 46 times in 14 packages SEXP SETCADR(SEXP x, SEXP y); // SETCADR used 112 times in 37 packages SEXP SETCADDR(SEXP x, SEXP y); // SETCADDR used 45 times in 14 packages SEXP SETCADDDR(SEXP x, SEXP y); // SETCADDDR used 31 times in 12 packages SEXP SETCAD4R(SEXP e, SEXP y); // SETCAD4R used 15 times in kergp, Sim.DiffProc, tikzDevice SEXP CONS_NR(SEXP a, SEXP b); // CONS_NR unused SEXP (FORMALS)(SEXP x); // FORMALS used 15 times in qtpaint, RSclient, PBSddesolve, Rserve, covr, pryr, rgp, testthat, RandomFields SEXP (BODY)(SEXP x); // BODY used 48 times in 15 packages SEXP (CLOENV)(SEXP x); // CLOENV used 23 times in Rcpp11, covr, pomp, Rcpp, pryr, testthat, qtbase int (RDEBUG)(SEXP x); // RDEBUG used 69 times in rmetasim int (RSTEP)(SEXP x); // RSTEP unused int (RTRACE)(SEXP x); // RTRACE unused void (SET_RDEBUG)(SEXP x, int v); // SET_RDEBUG unused void (SET_RSTEP)(SEXP x, int v); // SET_RSTEP unused void (SET_RTRACE)(SEXP x, int v); // SET_RTRACE unused void SET_FORMALS(SEXP x, SEXP v); // SET_FORMALS used 5 times in covr, rgp, testthat, qtbase void SET_BODY(SEXP x, SEXP v); // SET_BODY used 6 times in covr, rgp, testthat, qtbase void SET_CLOENV(SEXP x, SEXP v); // SET_CLOENV used 6 times in covr, rgp, testthat, qtbase SEXP (PRINTNAME)(SEXP x); // PRINTNAME used 92 times in 29 packages SEXP (SYMVALUE)(SEXP x); // SYMVALUE unused SEXP (INTERNAL)(SEXP x); // INTERNAL used 1014 times in 63 packages int (DDVAL)(SEXP x); // DDVAL unused void (SET_DDVAL)(SEXP x, int v); // SET_DDVAL unused void SET_PRINTNAME(SEXP x, SEXP v); // SET_PRINTNAME unused void SET_SYMVALUE(SEXP x, SEXP v); // SET_SYMVALUE unused void SET_INTERNAL(SEXP x, SEXP v); // SET_INTERNAL unused SEXP (FRAME)(SEXP x); // FRAME used 19 times in deTestSet, IRISSeismic, pryr, BayesBridge, datamap, BayesLogit SEXP (ENCLOS)(SEXP x); // ENCLOS used 7 times in Rcpp, pryr, rJava, Rcpp11, RGtk2 SEXP (HASHTAB)(SEXP x); // HASHTAB used 12 times in Rcpp, pryr, datamap, Rcpp11, qtbase int (ENVFLAGS)(SEXP x); // ENVFLAGS unused void (SET_ENVFLAGS)(SEXP x, int v); // SET_ENVFLAGS unused void SET_FRAME(SEXP x, SEXP v); // SET_FRAME used 4 times in rgp, mmap, qtbase void SET_ENCLOS(SEXP x, SEXP v); // SET_ENCLOS used 7 times in rgp, RandomFields, mmap, qtbase void SET_HASHTAB(SEXP x, SEXP v); // SET_HASHTAB used 5 times in rgp, mmap, qtbase SEXP (PRCODE)(SEXP x); // PRCODE used 15 times in dplyr, Rcpp, pryr, Rcpp11 SEXP (PRENV)(SEXP x); // PRENV used 14 times in igraph, dplyr, Rcpp, pryr, Rcpp11, lazyeval SEXP (PRVALUE)(SEXP x); // PRVALUE used 12 times in dplyr, Rcpp, pryr, Rcpp11 int (PRSEEN)(SEXP x); // PRSEEN used 4 times in Rcpp, Rcpp11 void (SET_PRSEEN)(SEXP x, int v); // SET_PRSEEN unused void SET_PRENV(SEXP x, SEXP v); // SET_PRENV unused void SET_PRVALUE(SEXP x, SEXP v); // SET_PRVALUE unused void SET_PRCODE(SEXP x, SEXP v); // SET_PRCODE unused void SET_PRSEEN(SEXP x, int v); // SET_PRSEEN unused int (HASHASH)(SEXP x); // HASHASH unused int (HASHVALUE)(SEXP x); // HASHVALUE unused void (SET_HASHASH)(SEXP x, int v); // SET_HASHASH unused void (SET_HASHVALUE)(SEXP x, int v); // SET_HASHVALUE unused typedef int PROTECT_INDEX; // PROTECT_INDEX used 94 times in 27 packages extern SEXP R_GlobalEnv; // R_GlobalEnv used 1400 times in 79 packages extern SEXP R_EmptyEnv; // R_EmptyEnv used 16 times in Rserve, dplR, Rcpp11, Rcpp, RcppClassic, pryr, rJava, adaptivetau, qtbase extern SEXP R_BaseEnv; // R_BaseEnv used 27 times in 15 packages extern SEXP R_BaseNamespace; // R_BaseNamespace used 3 times in Rcpp, Rcpp11 extern SEXP R_NamespaceRegistry; // R_NamespaceRegistry used 3 times in devtools, namespace, Rcpp extern SEXP R_Srcref; // R_Srcref unused extern SEXP R_NilValue; // R_NilValue used 10178 times in 491 packages extern SEXP R_UnboundValue; // R_UnboundValue used 73 times in 23 packages extern SEXP R_MissingArg; // R_MissingArg used 21 times in 12 packages extern SEXP R_RestartToken; // R_RestartToken unused extern SEXP R_baseSymbol; // R_baseSymbol unused extern SEXP R_BaseSymbol; // R_BaseSymbol unused extern SEXP R_BraceSymbol; // R_BraceSymbol unused extern SEXP R_Bracket2Symbol; // R_Bracket2Symbol used 4 times in purrr extern SEXP R_BracketSymbol; // R_BracketSymbol unused extern SEXP R_ClassSymbol; // R_ClassSymbol used 311 times in 84 packages extern SEXP R_DeviceSymbol; // R_DeviceSymbol unused extern SEXP R_DimNamesSymbol; // R_DimNamesSymbol used 230 times in 51 packages extern SEXP R_DimSymbol; // R_DimSymbol used 1015 times in 170 packages extern SEXP R_DollarSymbol; // R_DollarSymbol used 6 times in dplyr, Rcpp, Rcpp11 extern SEXP R_DotsSymbol; // R_DotsSymbol used 13 times in RPostgreSQL, RcppDE, lbfgs, purrr, RMySQL, DEoptim, qtbase extern SEXP R_DoubleColonSymbol; // R_DoubleColonSymbol unused extern SEXP R_DropSymbol; // R_DropSymbol unused extern SEXP R_LastvalueSymbol; // R_LastvalueSymbol unused extern SEXP R_LevelsSymbol; // R_LevelsSymbol used 51 times in 17 packages extern SEXP R_ModeSymbol; // R_ModeSymbol unused extern SEXP R_NaRmSymbol; // R_NaRmSymbol used 2 times in dplyr extern SEXP R_NameSymbol; // R_NameSymbol used 2 times in qtbase extern SEXP R_NamesSymbol; // R_NamesSymbol used 1373 times in 249 packages extern SEXP R_NamespaceEnvSymbol; // R_NamespaceEnvSymbol unused extern SEXP R_PackageSymbol; // R_PackageSymbol used 2 times in Rmosek, HiPLARM extern SEXP R_PreviousSymbol; // R_PreviousSymbol unused extern SEXP R_QuoteSymbol; // R_QuoteSymbol unused extern SEXP R_RowNamesSymbol; // R_RowNamesSymbol used 97 times in 37 packages extern SEXP R_SeedsSymbol; // R_SeedsSymbol used 2 times in treatSens extern SEXP R_SortListSymbol; // R_SortListSymbol unused extern SEXP R_SourceSymbol; // R_SourceSymbol unused extern SEXP R_SpecSymbol; // R_SpecSymbol unused extern SEXP R_TripleColonSymbol; // R_TripleColonSymbol unused extern SEXP R_TspSymbol; // R_TspSymbol unused extern SEXP R_dot_defined; // R_dot_defined unused extern SEXP R_dot_Method; // R_dot_Method unused extern SEXP R_dot_packageName; // R_dot_packageName unused extern SEXP R_dot_target; // R_dot_target unused extern SEXP R_NaString; // R_NaString used 36 times in stringdist, RCurl, RSclient, uniqueAtomMat, XML, Rserve, Rblpapi, SoundexBR, rJava, iotools // NA_STRING used 574 times in 90 packages extern SEXP R_BlankString; // R_BlankString used 39 times in 13 packages extern SEXP R_BlankScalarString; // R_BlankScalarString unused SEXP R_GetCurrentSrcref(int); // R_GetCurrentSrcref unused SEXP R_GetSrcFilename(SEXP); // R_GetSrcFilename unused SEXP Rf_asChar(SEXP); // Rf_asChar used 246 times in 16 packages // asChar used 194 times in 36 packages SEXP Rf_coerceVector(SEXP, SEXPTYPE); // Rf_coerceVector used 44 times in 13 packages // coerceVector used 2585 times in 167 packages SEXP Rf_PairToVectorList(SEXP x); // Rf_PairToVectorList unused // PairToVectorList used 7 times in cba, rcdd SEXP Rf_VectorToPairList(SEXP x); // Rf_VectorToPairList unused // VectorToPairList used 13 times in pomp, arules SEXP Rf_asCharacterFactor(SEXP x); // Rf_asCharacterFactor used 3 times in tidyr, reshape2, RSQLite // asCharacterFactor used 11 times in fastmatch, Kmisc, data.table int Rf_asLogical(SEXP x); // Rf_asLogical used 45 times in 11 packages // asLogical used 462 times in 64 packages int Rf_asInteger(SEXP x); // Rf_asInteger used 746 times in 23 packages // asInteger used 1277 times in 140 packages double Rf_asReal(SEXP x); // Rf_asReal used 113 times in 17 packages // asReal used 383 times in 83 packages Rcomplex Rf_asComplex(SEXP x); // Rf_asComplex unused // asComplex used 1 times in ff typedef struct R_allocator R_allocator_t; char * Rf_acopy_string(const char *); // Rf_acopy_string unused // acopy_string used 10 times in splusTimeDate void Rf_addMissingVarsToNewEnv(SEXP, SEXP); // Rf_addMissingVarsToNewEnv unused // addMissingVarsToNewEnv unused SEXP Rf_alloc3DArray(SEXPTYPE, int, int, int); // Rf_alloc3DArray unused // alloc3DArray used 21 times in mcmc, msm, TPmsm, unfoldr, RandomFields, cplm SEXP Rf_allocArray(SEXPTYPE, SEXP); // Rf_allocArray used 4 times in h5 // allocArray used 24 times in unfoldr, kergp, pomp, proxy, kza, slam, mvMORPH, TPmsm, ouch, RandomFields SEXP Rf_allocFormalsList2(SEXP sym1, SEXP sym2); // Rf_allocFormalsList2 unused // allocFormalsList2 unused SEXP Rf_allocFormalsList3(SEXP sym1, SEXP sym2, SEXP sym3); // Rf_allocFormalsList3 unused // allocFormalsList3 unused SEXP Rf_allocFormalsList4(SEXP sym1, SEXP sym2, SEXP sym3, SEXP sym4); // Rf_allocFormalsList4 unused // allocFormalsList4 unused SEXP Rf_allocFormalsList5(SEXP sym1, SEXP sym2, SEXP sym3, SEXP sym4, SEXP sym5); // Rf_allocFormalsList5 unused // allocFormalsList5 unused SEXP Rf_allocFormalsList6(SEXP sym1, SEXP sym2, SEXP sym3, SEXP sym4, SEXP sym5, SEXP sym6); // Rf_allocFormalsList6 unused // allocFormalsList6 unused SEXP Rf_allocMatrix(SEXPTYPE, int, int); // Rf_allocMatrix used 122 times in 14 packages // allocMatrix used 1577 times in 244 packages SEXP Rf_allocList(int); // Rf_allocList unused // allocList used 60 times in 25 packages SEXP Rf_allocS4Object(void); // Rf_allocS4Object used 2 times in Rserve, RSclient // allocS4Object used 1 times in arules SEXP Rf_allocSExp(SEXPTYPE); // Rf_allocSExp unused // allocSExp used 14 times in igraph, rgp, data.table, RandomFields, mmap, qtbase SEXP Rf_allocVector3(SEXPTYPE, R_xlen_t, R_allocator_t*); // Rf_allocVector3 unused // allocVector3 unused R_xlen_t Rf_any_duplicated(SEXP x, Rboolean from_last); // Rf_any_duplicated unused // any_duplicated used 5 times in data.table, checkmate R_xlen_t Rf_any_duplicated3(SEXP x, SEXP incomp, Rboolean from_last); // Rf_any_duplicated3 unused // any_duplicated3 unused SEXP Rf_applyClosure(SEXP, SEXP, SEXP, SEXP, SEXP); // Rf_applyClosure unused // applyClosure unused SEXP Rf_arraySubscript(int, SEXP, SEXP, SEXP (*)(SEXP,SEXP), SEXP (*)(SEXP, int), SEXP); SEXP Rf_classgets(SEXP, SEXP); // Rf_classgets used 27 times in fts, clpAPI, cplexAPI, sybilSBML, Rblpapi, glpkAPI // classgets used 91 times in 30 packages SEXP Rf_cons(SEXP, SEXP); // Rf_cons used 39 times in dplyr, Rcpp, Rcpp11 // cons used 609 times in 39 packages void Rf_copyMatrix(SEXP, SEXP, Rboolean); // Rf_copyMatrix used 8 times in CNVassoc // copyMatrix used 7 times in BDgraph, Matrix, kza void Rf_copyListMatrix(SEXP, SEXP, Rboolean); // Rf_copyListMatrix unused // copyListMatrix used 1 times in Matrix void Rf_copyMostAttrib(SEXP, SEXP); // Rf_copyMostAttrib used 8 times in tidyr, purrr, Rcpp, reshape2 // copyMostAttrib used 68 times in arules, robustbase, data.table, xts, memisc, proxy, zoo, tau void Rf_copyVector(SEXP, SEXP); // Rf_copyVector unused // copyVector used 12 times in tm, kza, mlegp, adaptivetau int Rf_countContexts(int, int); // Rf_countContexts unused // countContexts unused SEXP Rf_CreateTag(SEXP); // Rf_CreateTag unused // CreateTag used 1 times in rgp void Rf_defineVar(SEXP, SEXP, SEXP); // Rf_defineVar used 7 times in purrr, Rcpp, Rserve, Rcpp11 // defineVar used 218 times in 38 packages SEXP Rf_dimgets(SEXP, SEXP); // Rf_dimgets unused // dimgets used 3 times in CorrBin SEXP Rf_dimnamesgets(SEXP, SEXP); // Rf_dimnamesgets unused // dimnamesgets used 24 times in Matrix, RxCEcolInf, lxb, sapa SEXP Rf_DropDims(SEXP); // Rf_DropDims unused // DropDims unused SEXP Rf_duplicate(SEXP); // Rf_duplicate used 21 times in XML, data.table, Rcpp11, lme4, dplyr, Rcpp, RcppClassic, grr, NMF, copula // duplicate used 2088 times in 224 packages SEXP Rf_shallow_duplicate(SEXP); // Rf_shallow_duplicate unused // shallow_duplicate used 2 times in tmlenet, smint SEXP Rf_lazy_duplicate(SEXP); // Rf_lazy_duplicate unused // lazy_duplicate unused SEXP Rf_duplicated(SEXP, Rboolean); // Rf_duplicated unused // duplicated used 402 times in 100 packages Rboolean R_envHasNoSpecialSymbols(SEXP); // R_envHasNoSpecialSymbols unused SEXP Rf_eval(SEXP, SEXP); // Rf_eval used 105 times in 24 packages // eval used 25178 times in 269 packages SEXP Rf_findFun(SEXP, SEXP); // Rf_findFun used 7 times in Rcpp, Rcpp11, littler, RGtk2 // findFun used 13 times in sprint, tikzDevice, yaml, unfoldr, TraMineR, RGtk2 SEXP Rf_findVar(SEXP, SEXP); // Rf_findVar used 19 times in R2SWF, Rcpp11, dplyr, Rcpp, pryr, rJava, littler, showtext // findVar used 1333 times in 24 packages SEXP Rf_findVarInFrame(SEXP, SEXP); // Rf_findVarInFrame used 7 times in RCurl, Rcpp, Rcpp11 // findVarInFrame used 101 times in 13 packages SEXP Rf_findVarInFrame3(SEXP, SEXP, Rboolean); // Rf_findVarInFrame3 used 1 times in pryr // findVarInFrame3 used 5 times in datamap SEXP Rf_getAttrib(SEXP, SEXP); // Rf_getAttrib used 256 times in 36 packages // getAttrib used 1930 times in 239 packages SEXP Rf_GetArrayDimnames(SEXP); // Rf_GetArrayDimnames unused // GetArrayDimnames unused SEXP Rf_GetColNames(SEXP); // Rf_GetColNames unused // GetColNames unused void Rf_GetMatrixDimnames(SEXP, SEXP*, SEXP*, const char**, const char**); // Rf_GetMatrixDimnames unused // GetMatrixDimnames used 2 times in Kmisc, optmatch SEXP Rf_GetOption(SEXP, SEXP); // Rf_GetOption unused // GetOption used 5 times in rgl, gmp, Cairo, RGtk2 SEXP Rf_GetOption1(SEXP); // Rf_GetOption1 used 5 times in RProtoBuf, gmp // GetOption1 used 1 times in PCICt int Rf_GetOptionDigits(void); // Rf_GetOptionDigits unused // GetOptionDigits unused int Rf_GetOptionWidth(void); // Rf_GetOptionWidth used 1 times in progress // GetOptionWidth unused SEXP Rf_GetRowNames(SEXP); // Rf_GetRowNames unused // GetRowNames unused void Rf_gsetVar(SEXP, SEXP, SEXP); // Rf_gsetVar unused // gsetVar used 4 times in RSVGTipsDevice, Cairo, RSvgDevice, JavaGD SEXP Rf_install(const char *); // Rf_install used 990 times in 50 packages // install used 3178 times in 224 packages SEXP Rf_installChar(SEXP); // Rf_installChar used 15 times in dplyr, Rcpp // installChar used 4 times in dplyr SEXP Rf_installDDVAL(int i); // Rf_installDDVAL unused // installDDVAL unused SEXP Rf_installS3Signature(const char *, const char *); // Rf_installS3Signature unused // installS3Signature unused Rboolean Rf_isFree(SEXP); // Rf_isFree unused // isFree unused Rboolean Rf_isOrdered(SEXP); // Rf_isOrdered unused // isOrdered used 65 times in partykit, PythonInR, data.table, RSQLite Rboolean Rf_isUnordered(SEXP); // Rf_isUnordered used 1 times in OpenMx // isUnordered used 2 times in PythonInR Rboolean Rf_isUnsorted(SEXP, Rboolean); // Rf_isUnsorted unused // isUnsorted unused SEXP Rf_lengthgets(SEXP, R_len_t); // Rf_lengthgets used 7 times in readxl, readr // lengthgets used 47 times in 11 packages SEXP Rf_xlengthgets(SEXP, R_xlen_t); // Rf_xlengthgets unused // xlengthgets unused SEXP R_lsInternal(SEXP, Rboolean); // R_lsInternal used 5 times in Rcpp, rJava, Rcpp11, qtbase SEXP R_lsInternal3(SEXP, Rboolean, Rboolean); // R_lsInternal3 unused SEXP Rf_match(SEXP, SEXP, int); // Rf_match used 2 times in Rvcg // match used 8773 times in 388 packages SEXP Rf_matchE(SEXP, SEXP, int, SEXP); // Rf_matchE unused // matchE unused SEXP Rf_namesgets(SEXP, SEXP); // Rf_namesgets used 4 times in OpenMx, rpf // namesgets used 80 times in 14 packages SEXP Rf_mkChar(const char *); // Rf_mkChar used 517 times in 32 packages // mkChar used 4545 times in 287 packages SEXP Rf_mkCharLen(const char *, int); // Rf_mkCharLen used 21 times in refGenome, redland, Rcpp11, stringi, Kmisc, Rcpp, sourcetools, iotools // mkCharLen used 38 times in 16 packages Rboolean Rf_NonNullStringMatch(SEXP, SEXP); // Rf_NonNullStringMatch unused // NonNullStringMatch used 8 times in proxy, arules, arulesSequences, cba int Rf_ncols(SEXP); // Rf_ncols used 22 times in fdaPDE, fts, BoomSpikeSlab, Rmosek, ccgarch, rcppbugs, biganalytics, CEC, OpenMx, RTriangle // ncols used 3805 times in 182 packages int Rf_nrows(SEXP); // Rf_nrows used 32 times in 12 packages // nrows used 4332 times in 215 packages SEXP Rf_nthcdr(SEXP, int); // Rf_nthcdr unused // nthcdr used 9 times in sprint, rmongodb, PythonInR, xts typedef enum {Bytes, Chars, Width} nchar_type; int R_nchar(SEXP string, nchar_type type_, // R_nchar unused Rboolean allowNA, Rboolean keepNA, const char* msg_name); Rboolean Rf_pmatch(SEXP, SEXP, Rboolean); // Rf_pmatch unused // pmatch used 169 times in ore, git2r, AdaptFitOS, data.table, seqminer, locfit, oce, rmumps Rboolean Rf_psmatch(const char *, const char *, Rboolean); // Rf_psmatch unused // psmatch used 5 times in rgl void Rf_PrintValue(SEXP); // Rf_PrintValue used 95 times in 19 packages // PrintValue used 119 times in 13 packages void Rf_readS3VarsFromFrame(SEXP, SEXP*, SEXP*, SEXP*, SEXP*, SEXP*, SEXP*); // Rf_readS3VarsFromFrame unused // readS3VarsFromFrame unused SEXP Rf_setAttrib(SEXP, SEXP, SEXP); // Rf_setAttrib used 325 times in 35 packages // setAttrib used 1830 times in 251 packages void Rf_setSVector(SEXP*, int, SEXP); // Rf_setSVector unused // setSVector unused void Rf_setVar(SEXP, SEXP, SEXP); // Rf_setVar used 1 times in showtext // setVar used 24 times in Rhpc, rscproxy, PythonInR, rgenoud, survival, gsl, littler, spatstat SEXP Rf_stringSuffix(SEXP, int); // Rf_stringSuffix unused // stringSuffix unused SEXPTYPE Rf_str2type(const char *); // Rf_str2type used 4 times in purrr // str2type used 1 times in RGtk2 Rboolean Rf_StringBlank(SEXP); // Rf_StringBlank used 1 times in LCMCR // StringBlank unused SEXP Rf_substitute(SEXP,SEXP); // Rf_substitute unused // substitute used 255 times in 56 packages const char * Rf_translateChar(SEXP); // Rf_translateChar used 1 times in devEMF // translateChar used 59 times in 19 packages const char * Rf_translateChar0(SEXP); // Rf_translateChar0 unused // translateChar0 unused const char * Rf_translateCharUTF8(SEXP); // Rf_translateCharUTF8 used 22 times in Rserve, xml2, readr, gdtools, Rcpp11, dplyr, Rcpp, haven // translateCharUTF8 used 66 times in 13 packages const char * Rf_type2char(SEXPTYPE); // Rf_type2char used 33 times in 13 packages // type2char used 107 times in 12 packages SEXP Rf_type2rstr(SEXPTYPE); // Rf_type2rstr unused // type2rstr unused SEXP Rf_type2str(SEXPTYPE); // Rf_type2str used 4 times in Rcpp, pryr // type2str used 3 times in Kmisc, yaml SEXP Rf_type2str_nowarn(SEXPTYPE); // Rf_type2str_nowarn unused // type2str_nowarn used 1 times in qrmtools void Rf_unprotect_ptr(SEXP); // Rf_unprotect_ptr unused // unprotect_ptr unused void __attribute__((noreturn)) R_signal_protect_error(void); void __attribute__((noreturn)) R_signal_unprotect_error(void); void __attribute__((noreturn)) R_signal_reprotect_error(PROTECT_INDEX i); SEXP R_tryEval(SEXP, SEXP, int *); // R_tryEval used 1118 times in 24 packages SEXP R_tryEvalSilent(SEXP, SEXP, int *); // R_tryEvalSilent unused const char *R_curErrorBuf(); // R_curErrorBuf used 4 times in Rhpc, Rcpp11 Rboolean Rf_isS4(SEXP); // Rf_isS4 used 16 times in Rcpp, Rcpp11 // isS4 used 13 times in PythonInR, Rcpp11, dplyr, Rcpp, catnet, rmumps, sdnet SEXP Rf_asS4(SEXP, Rboolean, int); // Rf_asS4 unused // asS4 unused SEXP Rf_S3Class(SEXP); // Rf_S3Class unused // S3Class used 4 times in RInside, littler int Rf_isBasicClass(const char *); // Rf_isBasicClass unused // isBasicClass unused Rboolean R_cycle_detected(SEXP s, SEXP child); // R_cycle_detected unused typedef enum { CE_NATIVE = 0, CE_UTF8 = 1, CE_LATIN1 = 2, CE_BYTES = 3, CE_SYMBOL = 5, CE_ANY =99 } cetype_t; // cetype_t used 47 times in 13 packages cetype_t Rf_getCharCE(SEXP); // Rf_getCharCE used 13 times in RSclient, Rserve, genie, dplyr, Rcpp, rJava, ROracle // getCharCE used 16 times in ore, RSclient, PythonInR, Rserve, jsonlite, tau, rJava SEXP Rf_mkCharCE(const char *, cetype_t); // Rf_mkCharCE used 40 times in readxl, mongolite, xml2, readr, Rcpp11, stringi, commonmark, dplyr, Rcpp, haven // mkCharCE used 72 times in 15 packages SEXP Rf_mkCharLenCE(const char *, int, cetype_t); // Rf_mkCharLenCE used 68 times in readr, ROracle, stringi // mkCharLenCE used 23 times in 11 packages const char *Rf_reEnc(const char *x, cetype_t ce_in, cetype_t ce_out, int subst); // Rf_reEnc used 5 times in RCurl, RSclient, Rserve, rJava // reEnc used 3 times in PythonInR, RJSONIO SEXP R_forceAndCall(SEXP e, int n, SEXP rho); // R_forceAndCall unused SEXP R_MakeExternalPtr(void *p, SEXP tag, SEXP prot); // R_MakeExternalPtr used 321 times in 102 packages void *R_ExternalPtrAddr(SEXP s); // R_ExternalPtrAddr used 2127 times in 115 packages SEXP R_ExternalPtrTag(SEXP s); // R_ExternalPtrTag used 195 times in 32 packages SEXP R_ExternalPtrProtected(SEXP s); // R_ExternalPtrProtected used 6 times in PopGenome, Rcpp, WhopGenome, data.table, Rcpp11 void R_ClearExternalPtr(SEXP s); // R_ClearExternalPtr used 157 times in 64 packages void R_SetExternalPtrAddr(SEXP s, void *p); // R_SetExternalPtrAddr used 23 times in ff, PopGenome, RCurl, rstream, Rlabkey, WhopGenome, XML, RJSONIO, memisc, ROracle void R_SetExternalPtrTag(SEXP s, SEXP tag); // R_SetExternalPtrTag used 16 times in PopGenome, rstream, Rlabkey, WhopGenome, Rcpp11, Rcpp, rLindo void R_SetExternalPtrProtected(SEXP s, SEXP p); // R_SetExternalPtrProtected used 9 times in PopGenome, rstream, Rlabkey, Rcpp, WhopGenome, Rcpp11 typedef void (*R_CFinalizer_t)(SEXP); void R_RegisterFinalizer(SEXP s, SEXP fun); // R_RegisterFinalizer used 1 times in XML void R_RegisterCFinalizer(SEXP s, R_CFinalizer_t fun); // R_RegisterCFinalizer used 73 times in 27 packages void R_RegisterFinalizerEx(SEXP s, SEXP fun, Rboolean onexit); // R_RegisterFinalizerEx unused void R_RegisterCFinalizerEx(SEXP s, R_CFinalizer_t fun, Rboolean onexit); // R_RegisterCFinalizerEx used 152 times in 58 packages void R_RunPendingFinalizers(void); // R_RunPendingFinalizers unused SEXP R_MakeWeakRef(SEXP key, SEXP val, SEXP fin, Rboolean onexit); // R_MakeWeakRef used 4 times in igraph, svd SEXP R_MakeWeakRefC(SEXP key, SEXP val, R_CFinalizer_t fin, Rboolean onexit); // R_MakeWeakRefC unused SEXP R_WeakRefKey(SEXP w); // R_WeakRefKey used 3 times in igraph, Rcpp, Rcpp11 SEXP R_WeakRefValue(SEXP w); // R_WeakRefValue used 7 times in igraph, Rcpp, svd, Rcpp11 void R_RunWeakRefFinalizer(SEXP w); // R_RunWeakRefFinalizer used 1 times in igraph SEXP R_PromiseExpr(SEXP); // R_PromiseExpr unused SEXP R_ClosureExpr(SEXP); // R_ClosureExpr unused void R_initialize_bcode(void); // R_initialize_bcode unused SEXP R_bcEncode(SEXP); // R_bcEncode unused SEXP R_bcDecode(SEXP); // R_bcDecode unused Rboolean R_ToplevelExec(void (*fun)(void *), void *data); SEXP R_ExecWithCleanup(SEXP (*fun)(void *), void *data, void (*cleanfun)(void *), void *cleandata); void R_RestoreHashCount(SEXP rho); // R_RestoreHashCount unused Rboolean R_IsPackageEnv(SEXP rho); // R_IsPackageEnv unused SEXP R_PackageEnvName(SEXP rho); // R_PackageEnvName unused SEXP R_FindPackageEnv(SEXP info); // R_FindPackageEnv unused Rboolean R_IsNamespaceEnv(SEXP rho); // R_IsNamespaceEnv unused SEXP R_NamespaceEnvSpec(SEXP rho); // R_NamespaceEnvSpec unused SEXP R_FindNamespace(SEXP info); // R_FindNamespace used 14 times in 11 packages void R_LockEnvironment(SEXP env, Rboolean bindings); // R_LockEnvironment used 2 times in Rcpp, Rcpp11 Rboolean R_EnvironmentIsLocked(SEXP env); // R_EnvironmentIsLocked used 2 times in Rcpp, Rcpp11 void R_LockBinding(SEXP sym, SEXP env); // R_LockBinding used 3 times in data.table, Rcpp, Rcpp11 void R_unLockBinding(SEXP sym, SEXP env); // R_unLockBinding used 2 times in Rcpp, Rcpp11 void R_MakeActiveBinding(SEXP sym, SEXP fun, SEXP env); // R_MakeActiveBinding unused Rboolean R_BindingIsLocked(SEXP sym, SEXP env); // R_BindingIsLocked used 2 times in Rcpp, Rcpp11 Rboolean R_BindingIsActive(SEXP sym, SEXP env); // R_BindingIsActive used 2 times in Rcpp, Rcpp11 Rboolean R_HasFancyBindings(SEXP rho); // R_HasFancyBindings unused void Rf_errorcall(SEXP, const char *, ...) __attribute__((noreturn)); // Rf_errorcall used 27 times in purrr, mongolite, jsonlite, pbdMPI, rJava, openssl // errorcall used 103 times in RCurl, arules, XML, arulesSequences, pbdMPI, xts, proxy, cba, rJava, RSAP void Rf_warningcall(SEXP, const char *, ...); // Rf_warningcall used 5 times in pbdMPI, mongolite // warningcall used 4 times in RInside, jsonlite, pbdMPI void Rf_warningcall_immediate(SEXP, const char *, ...); // Rf_warningcall_immediate used 2 times in mongolite, V8 // warningcall_immediate used 2 times in Runuran void R_XDREncodeDouble(double d, void *buf); // R_XDREncodeDouble unused double R_XDRDecodeDouble(void *buf); // R_XDRDecodeDouble unused void R_XDREncodeInteger(int i, void *buf); // R_XDREncodeInteger unused int R_XDRDecodeInteger(void *buf); // R_XDRDecodeInteger unused typedef void *R_pstream_data_t; typedef enum { R_pstream_any_format, R_pstream_ascii_format, R_pstream_binary_format, R_pstream_xdr_format, R_pstream_asciihex_format } R_pstream_format_t; // R_pstream_format_t used 7 times in RApiSerialize, Rhpc, fastdigest typedef struct R_outpstream_st *R_outpstream_t; struct R_outpstream_st { R_pstream_data_t data; R_pstream_format_t type; int version; void (*OutChar)(R_outpstream_t, int); void (*OutBytes)(R_outpstream_t, void *, int); SEXP (*OutPersistHookFunc)(SEXP, SEXP); SEXP OutPersistHookData; // OutPersistHookData unused }; typedef struct R_inpstream_st *R_inpstream_t; struct R_inpstream_st { R_pstream_data_t data; R_pstream_format_t type; int (*InChar)(R_inpstream_t); void (*InBytes)(R_inpstream_t, void *, int); SEXP (*InPersistHookFunc)(SEXP, SEXP); SEXP InPersistHookData; // InPersistHookData unused }; void R_InitInPStream(R_inpstream_t stream, R_pstream_data_t data, // R_InitInPStream used 2 times in RApiSerialize, Rhpc R_pstream_format_t type, int (*inchar)(R_inpstream_t), void (*inbytes)(R_inpstream_t, void *, int), SEXP (*phook)(SEXP, SEXP), SEXP pdata); void R_InitOutPStream(R_outpstream_t stream, R_pstream_data_t data, // R_InitOutPStream used 4 times in RApiSerialize, Rhpc, fastdigest, qtbase R_pstream_format_t type, int version, void (*outchar)(R_outpstream_t, int), void (*outbytes)(R_outpstream_t, void *, int), SEXP (*phook)(SEXP, SEXP), SEXP pdata); void R_InitFileInPStream(R_inpstream_t stream, FILE *fp, // R_InitFileInPStream used 1 times in filehash R_pstream_format_t type, SEXP (*phook)(SEXP, SEXP), SEXP pdata); void R_InitFileOutPStream(R_outpstream_t stream, FILE *fp, // R_InitFileOutPStream unused R_pstream_format_t type, int version, SEXP (*phook)(SEXP, SEXP), SEXP pdata); void R_Serialize(SEXP s, R_outpstream_t ops); // R_Serialize used 4 times in RApiSerialize, Rhpc, fastdigest, qtbase SEXP R_Unserialize(R_inpstream_t ips); // R_Unserialize used 4 times in RApiSerialize, Rhpc, filehash SEXP R_do_slot(SEXP obj, SEXP name); // R_do_slot used 162 times in 11 packages SEXP R_do_slot_assign(SEXP obj, SEXP name, SEXP value); // R_do_slot_assign used 17 times in excel.link, redland, Rcpp11, Matrix, TMB, Rcpp, FREGAT, HiPLARM, OpenMx, rJPSGCS int R_has_slot(SEXP obj, SEXP name); // R_has_slot used 14 times in Matrix, Rcpp, HiPLARM, OpenMx, Rcpp11 SEXP R_do_MAKE_CLASS(const char *what); // R_do_MAKE_CLASS used 6 times in TMB, Rcpp, Rcpp11 SEXP R_getClassDef (const char *what); // R_getClassDef used 5 times in memisc, Rcpp, Rcpp11 SEXP R_getClassDef_R(SEXP what); // R_getClassDef_R unused Rboolean R_has_methods_attached(void); // R_has_methods_attached unused Rboolean R_isVirtualClass(SEXP class_def, SEXP env); // R_isVirtualClass unused Rboolean R_extends (SEXP class1, SEXP class2, SEXP env); // R_extends unused SEXP R_do_new_object(SEXP class_def); // R_do_new_object used 9 times in TMB, memisc, Rcpp, Rcpp11 int R_check_class_and_super(SEXP x, const char **valid, SEXP rho); // R_check_class_and_super used 5 times in Matrix, Rmosek, HiPLARM int R_check_class_etc (SEXP x, const char **valid); // R_check_class_etc used 41 times in Matrix, HiPLARM void R_PreserveObject(SEXP); // R_PreserveObject used 112 times in 29 packages void R_ReleaseObject(SEXP); // R_ReleaseObject used 114 times in 27 packages void R_dot_Last(void); // R_dot_Last used 4 times in RInside, rJava, littler void R_RunExitFinalizers(void); // R_RunExitFinalizers used 4 times in RInside, TMB, rJava, littler int R_system(const char *); // R_system used 1 times in rJava Rboolean R_compute_identical(SEXP, SEXP, int); // R_compute_identical used 14 times in igraph, Matrix, rgp, data.table void R_orderVector(int *indx, int n, SEXP arglist, Rboolean nalast, Rboolean decreasing); // R_orderVector used 5 times in glpkAPI, nontarget, CEGO SEXP Rf_allocVector(SEXPTYPE, R_xlen_t); // Rf_allocVector used 1086 times in 59 packages // allocVector used 12419 times in 551 packages Rboolean Rf_conformable(SEXP, SEXP); // Rf_conformable unused // conformable used 141 times in 22 packages SEXP Rf_elt(SEXP, int); // Rf_elt unused // elt used 2310 times in 37 packages Rboolean Rf_inherits(SEXP, const char *); // Rf_inherits used 530 times in 21 packages // inherits used 814 times in 80 packages Rboolean Rf_isArray(SEXP); // Rf_isArray unused // isArray used 34 times in checkmate, PythonInR, data.table, ifultools, Rblpapi, Rvcg, unfoldr, TMB, kza, qtbase Rboolean Rf_isFactor(SEXP); // Rf_isFactor used 22 times in 11 packages // isFactor used 42 times in checkmate, rggobi, PythonInR, data.table, Kmisc, partykit, cba, qtbase, RSQLite Rboolean Rf_isFrame(SEXP); // Rf_isFrame used 1 times in OpenMx // isFrame used 15 times in checkmate, splusTimeDate, OjaNP, PythonInR, data.table, robfilter Rboolean Rf_isFunction(SEXP); // Rf_isFunction used 4 times in Rserve, genie, RcppClassic // isFunction used 274 times in 43 packages Rboolean Rf_isInteger(SEXP); // Rf_isInteger used 39 times in 14 packages // isInteger used 402 times in 77 packages Rboolean Rf_isLanguage(SEXP); // Rf_isLanguage unused // isLanguage used 63 times in PythonInR, rgp, RandomFields Rboolean Rf_isList(SEXP); // Rf_isList unused // isList used 40 times in 11 packages Rboolean Rf_isMatrix(SEXP); // Rf_isMatrix used 55 times in 16 packages // isMatrix used 293 times in 65 packages Rboolean Rf_isNewList(SEXP); // Rf_isNewList used 6 times in Rmosek, RcppClassic // isNewList used 103 times in 27 packages Rboolean Rf_isNumber(SEXP); // Rf_isNumber unused // isNumber used 14 times in PythonInR, readr, stringi, qtbase Rboolean Rf_isNumeric(SEXP); // Rf_isNumeric used 31 times in Rmosek, gaselect, RcppCNPy, genie, mets, Morpho, rstan, Rcpp, RcppClassic, OpenMx // isNumeric used 468 times in 49 packages Rboolean Rf_isPairList(SEXP); // Rf_isPairList unused // isPairList used 2 times in PythonInR Rboolean Rf_isPrimitive(SEXP); // Rf_isPrimitive unused // isPrimitive used 7 times in PythonInR, qtbase Rboolean Rf_isTs(SEXP); // Rf_isTs unused // isTs used 2 times in PythonInR Rboolean Rf_isUserBinop(SEXP); // Rf_isUserBinop unused // isUserBinop used 2 times in PythonInR Rboolean Rf_isValidString(SEXP); // Rf_isValidString unused // isValidString used 26 times in SSN, PythonInR, foreign, pbdMPI, RJSONIO, SASxport Rboolean Rf_isValidStringF(SEXP); // Rf_isValidStringF unused // isValidStringF used 2 times in PythonInR Rboolean Rf_isVector(SEXP); // Rf_isVector used 15 times in RProtoBuf, RcppCNPy, stringi, purrr, RcppClassic, OpenMx, adaptivetau // isVector used 182 times in 46 packages Rboolean Rf_isVectorAtomic(SEXP); // Rf_isVectorAtomic used 13 times in agop, tidyr, reshape2, stringi // isVectorAtomic used 40 times in bit, matrixStats, checkmate, PythonInR, data.table, Matrix, bit64, potts, aster2, qtbase Rboolean Rf_isVectorList(SEXP); // Rf_isVectorList used 23 times in genie, purrr, RNiftyReg, stringi // isVectorList used 12 times in RPostgreSQL, spsurvey, PythonInR, stringi, adaptivetau, PCICt, RandomFields Rboolean Rf_isVectorizable(SEXP); // Rf_isVectorizable unused // isVectorizable used 3 times in PythonInR, robfilter SEXP Rf_lang1(SEXP); // Rf_lang1 used 14 times in PopGenome, WhopGenome, nontarget, Rcpp11, purrr, Rcpp, CEGO // lang1 used 30 times in 11 packages SEXP Rf_lang2(SEXP, SEXP); // Rf_lang2 used 64 times in 13 packages // lang2 used 216 times in 75 packages SEXP Rf_lang3(SEXP, SEXP, SEXP); // Rf_lang3 used 19 times in purrr, RcppDE, Rcpp, lbfgs, emdist, Rcpp11 // lang3 used 107 times in 28 packages SEXP Rf_lang4(SEXP, SEXP, SEXP, SEXP); // Rf_lang4 used 8 times in lme4, purrr, Rcpp, diversitree, Rcpp11 // lang4 used 65 times in 21 packages SEXP Rf_lang5(SEXP, SEXP, SEXP, SEXP, SEXP); // Rf_lang5 unused // lang5 used 11 times in PBSddesolve, GNE, SMC SEXP Rf_lang6(SEXP, SEXP, SEXP, SEXP, SEXP, SEXP); // Rf_lang6 used 1 times in lme4 // lang6 used 2 times in GNE SEXP Rf_lastElt(SEXP); // Rf_lastElt unused // lastElt unused SEXP Rf_lcons(SEXP, SEXP); // Rf_lcons used 26 times in purrr, rcppbugs, Rcpp, pryr // lcons used 16 times in rmgarch R_len_t Rf_length(SEXP); // Rf_length used 662 times in 69 packages SEXP Rf_list1(SEXP); // Rf_list1 used 1 times in Rcpp // list1 used 197 times in 11 packages SEXP Rf_list2(SEXP, SEXP); // Rf_list2 unused // list2 used 441 times in 12 packages SEXP Rf_list3(SEXP, SEXP, SEXP); // Rf_list3 unused // list3 used 72 times in marked, Rdsdp, BH, svd SEXP Rf_list4(SEXP, SEXP, SEXP, SEXP); // Rf_list4 unused // list4 used 58 times in igraph, PBSddesolve, Rserve, BH, yaml, treethresh, SMC SEXP Rf_list5(SEXP, SEXP, SEXP, SEXP, SEXP); // Rf_list5 unused // list5 used 63 times in Rdsdp, BH SEXP Rf_listAppend(SEXP, SEXP); // Rf_listAppend unused // listAppend used 1 times in ore SEXP Rf_mkNamed(SEXPTYPE, const char **); // Rf_mkNamed used 8 times in Matrix, gmp, RSclient, HiPLARM // mkNamed used 12 times in RCassandra, coxme, SamplerCompare, survival, JavaGD, DEoptim, qtbase SEXP Rf_mkString(const char *); // Rf_mkString used 179 times in 24 packages // mkString used 814 times in 96 packages int Rf_nlevels(SEXP); // Rf_nlevels unused // nlevels used 546 times in 26 packages int Rf_stringPositionTr(SEXP, const char *); // Rf_stringPositionTr unused // stringPositionTr unused SEXP Rf_ScalarComplex(Rcomplex); // Rf_ScalarComplex unused // ScalarComplex unused SEXP Rf_ScalarInteger(int); // Rf_ScalarInteger used 390 times in 20 packages // ScalarInteger used 704 times in 88 packages SEXP Rf_ScalarLogical(int); // Rf_ScalarLogical used 160 times in 20 packages // ScalarLogical used 450 times in 64 packages SEXP Rf_ScalarRaw(Rbyte); // Rf_ScalarRaw unused // ScalarRaw used 4 times in qtbase, RGtk2 SEXP Rf_ScalarReal(double); // Rf_ScalarReal used 146 times in 19 packages // ScalarReal used 330 times in 65 packages SEXP Rf_ScalarString(SEXP); // Rf_ScalarString used 33 times in agop, Nippon, Rcpp11, rpf, stringi, purrr, Rcpp // ScalarString used 198 times in 37 packages R_xlen_t Rf_xlength(SEXP); // Rf_xlength used 46 times in WGCNA, Rcpp, Rcpp11 SEXP Rf_protect(SEXP); // Rf_protect used 332 times in 12 packages // protect used 599 times in 101 packages void Rf_unprotect(int); // Rf_unprotect used 289 times in 12 packages // unprotect used 110 times in 35 packages void R_ProtectWithIndex(SEXP, PROTECT_INDEX *); // R_ProtectWithIndex used 8 times in OpenMx void R_Reprotect(SEXP, PROTECT_INDEX); // R_Reprotect used 2 times in OpenMx SEXP R_FixupRHS(SEXP x, SEXP y); // R_FixupRHS unused } </pre> == Stats == <pre> 0 1 2 3 4 5 6 7 8 9 10+ Macro: 31 5 5 3 4 1 2 2 0 1 53 (usage count) (107) 31 11 7 1 5 1 3 3 2 2 41 (distinct package count) Function: 103 15 17 12 16 12 7 7 4 5 190 (usage count) (388) 103 31 32 22 29 7 9 7 1 6 141 (distinct package count) Variable: 26 0 4 2 1 0 1 0 0 0 20 (usage count) (54) 26 4 2 2 0 0 0 1 0 1 18 (distinct package count) TypeDef: 0 0 0 0 0 0 0 1 0 0 5 (usage count) (6) 0 0 0 1 0 0 0 0 0 0 5 (distinct package count) Alias: 40 9 9 8 6 4 0 3 1 1 97 (usage count) (178) 40 25 14 5 9 1 5 5 3 1 70 (distinct package count) </pre> (for a quick explanation of these stats see [[Native_API_stats_of_R.h]]) c7d34a6e03aad9a801fcab17651612b2ed743c14 0 10 18 2016-06-20T15:20:17Z Lukasstadler 8 Created page with "== Input == <pre> #define USE_RINTERNALS #include "Rembedded.h" #include "Rmath.h" #include "libintl.h" #include "R.h" #include "Rinterface.h" #include "Rdefines.h" #include "..." wikitext text/x-wiki == Input == <pre> #define USE_RINTERNALS #include "Rembedded.h" #include "Rmath.h" #include "libintl.h" #include "R.h" #include "Rinterface.h" #include "Rdefines.h" #include "Rinternals.h" #include "S.h" #include "R_ext/Applic.h" #include "R_ext/Arith.h" #include "R_ext/BLAS.h" #include "R_ext/Boolean.h" #include "R_ext/Callbacks.h" #include "R_ext/Complex.h" #include "R_ext/Connections.h" #include "R_ext/Constants.h" #include "R_ext/Error.h" #include "R_ext/eventloop.h" #include "R_ext/GetX11Image.h" #include "R_ext/GraphicsEngine.h" #include "R_ext/GraphicsDevice.h" #include "R_ext/Itermacros.h" #include "R_ext/Lapack.h" #include "R_ext/libextern.h" #include "R_ext/Linpack.h" #include "R_ext/MathThreads.h" #include "R_ext/Memory.h" #include "R_ext/Parse.h" #include "R_ext/Print.h" #include "R_ext/PrtUtil.h" #include "R_ext/QuartzDevice.h" #include "R_ext/R-ftp-http.h" #include "R_ext/Rallocators.h" #include "R_ext/Random.h" #include "R_ext/Rdynload.h" #include "R_ext/Riconv.h" #include "R_ext/RS.h" #include "R_ext/RStartup.h" #include "R_ext/stats_package.h" #include "R_ext/stats_stubs.h" #include "R_ext/Utils.h" #include "R_ext/Visibility.h" </pre> == Result == <pre> #define ANYSXP 18 // ANYSXP used 14 times in RPostgreSQL, Rcpp11, seqminer, Rcpp, pryr, rtkpp, rtkore, RGtk2 #define AS_CHARACTER(x) Rf_coerceVector(x,16) // AS_CHARACTER used 115 times in 27 packages #define AS_COMPLEX(x) Rf_coerceVector(x,15) // AS_COMPLEX used 28 times in PearsonDS, kza, diversitree #define AS_INTEGER(x) Rf_coerceVector(x,13) // AS_INTEGER used 753 times in 66 packages #define AS_LIST(x) Rf_coerceVector(x,19) // AS_LIST used 81 times in RPostgreSQL, lfe, CRF, memisc, catnet, polyclip, sdnet #define AS_LOGICAL(x) Rf_coerceVector(x,10) // AS_LOGICAL used 59 times in 13 packages #define AS_NUMERIC(x) Rf_coerceVector(x,14) // AS_NUMERIC used 1099 times in 71 packages #define AS_RAW(x) Rf_coerceVector(x,24) // AS_RAW used 24 times in CrypticIBDcheck, IRISSeismic, seqinr, oce #define AS_VECTOR(x) Rf_coerceVector(x,19) // AS_VECTOR used 3 times in catnet, PET, sdnet #define ATTRIB(x) ((x)->attrib) // ATTRIB used 83 times in 20 packages #define AdobeSymbol2utf8 Rf_AdobeSymbol2utf8 // AdobeSymbol2utf8 used 2 times in Cairo #define BCODESXP 21 // BCODESXP used 15 times in rcppbugs, Rcpp11, seqminer, Rcpp, pryr, rtkpp, rtkore #define BCODE_CODE(x) ((x)->u.listsxp.carval) // BCODE_CODE unused #define BCODE_CONSTS(x) ((x)->u.listsxp.cdrval) // BCODE_CONSTS unused #define BCODE_EXPR(x) ((x)->u.listsxp.tagval) // BCODE_EXPR unused #define BEGIN_SUSPEND_INTERRUPTS do { Rboolean __oldsusp__ = R_interrupts_suspended; R_interrupts_suspended = 1; // BEGIN_SUSPEND_INTERRUPTS used 22 times in 12 packages #define BLAS_extern extern // BLAS_extern used 2 times in sparseSEM #define BODY(x) ((x)->u.closxp.body) // BODY used 48 times in 15 packages #define BODY_EXPR(e) R_ClosureExpr(e) // BODY_EXPR unused #define BUILTINSXP 8 // BUILTINSXP used 24 times in 11 packages #define CAAR(e) ((((e)->u.listsxp.carval))->u.listsxp.carval) // CAAR unused #define CAD4R(e) ((((((((((e)->u.listsxp.cdrval))->u.listsxp.cdrval))->u.listsxp.cdrval))->u.listsxp.cdrval))->u.listsxp.carval) // CAD4R used 14 times in earth, foreign, actuar #define CADDDR(e) ((((((((e)->u.listsxp.cdrval))->u.listsxp.cdrval))->u.listsxp.cdrval))->u.listsxp.carval) // CADDDR used 21 times in RPostgreSQL, foreign, actuar, bibtex #define CADDR(e) ((((((e)->u.listsxp.cdrval))->u.listsxp.cdrval))->u.listsxp.carval) // CADDR used 52 times in 11 packages #define CADR(e) ((((e)->u.listsxp.cdrval))->u.listsxp.carval) // CADR used 104 times in 17 packages #define CAR(e) ((e)->u.listsxp.carval) // CAR used 575 times in 63 packages #define CDAR(e) ((((e)->u.listsxp.carval))->u.listsxp.cdrval) // CDAR unused #define CDDDR(e) ((((((e)->u.listsxp.cdrval))->u.listsxp.cdrval))->u.listsxp.cdrval) // CDDDR unused #define CDDR(e) ((((e)->u.listsxp.cdrval))->u.listsxp.cdrval) // CDDR used 52 times in Rlabkey, Rcpp11, dplyr, proxy, Rcpp, slam, tikzDevice, OpenCL, svd #define CDR(e) ((e)->u.listsxp.cdrval) // CDR used 4523 times in 76 packages #define CHAR(x) ((const char *) (((SEXPREC_ALIGN *) (x)) + 1)) // CHAR used 4405 times in 362 packages #define CHARACTER_DATA(x) (((SEXP *) (((SEXPREC_ALIGN *) (x)) + 1))) // CHARACTER_DATA used 22 times in pomp, rggobi, XML, RGtk2 #define CHARACTER_POINTER(x) ((SEXP *) (((SEXPREC_ALIGN *) (x)) + 1)) // CHARACTER_POINTER used 19 times in multic, RPostgreSQL, arules, Hmisc, lazy, R4dfp #define CHARACTER_VALUE(x) ((const char *) (((SEXPREC_ALIGN *) (Rf_asChar(x))) + 1)) // CHARACTER_VALUE used 269 times in 11 packages #define CHARSXP 9 // CHARSXP used 106 times in 33 packages #define CLOENV(x) ((x)->u.closxp.env) // CLOENV used 23 times in Rcpp11, covr, pomp, Rcpp, pryr, testthat, qtbase #define CLOSXP 3 // CLOSXP used 83 times in 30 packages #define COMPLEX(x) ((Rcomplex *) (((SEXPREC_ALIGN *) (x)) + 1)) // COMPLEX used 1697 times in 71 packages #define COMPLEX_DATA(x) (((Rcomplex *) (((SEXPREC_ALIGN *) (x)) + 1))) // COMPLEX_DATA unused #define COMPLEX_POINTER(x) ((Rcomplex *) (((SEXPREC_ALIGN *) (x)) + 1)) // COMPLEX_POINTER used 3 times in timsac, ifs #define CONS(a, b) Rf_cons((a), (b)) // CONS used 458 times in 30 packages #define COPY_TO_USER_STRING(x) Rf_mkChar(x) // COPY_TO_USER_STRING used 374 times in 21 packages #define CPLXSXP 15 // CPLXSXP used 409 times in 49 packages #define CREATE_FUNCTION_CALL(name, argList) createFunctionCall(name, argList) // CREATE_FUNCTION_CALL used 5 times in rggobi, XML, RGtk2 #define CREATE_STRING_VECTOR(x) Rf_mkChar(x) // CREATE_STRING_VECTOR used 244 times in igraph, rggobi, XML, dbarts, lazy, rwt, RGtk2 #define Calloc(n, t) (t *) R_chk_calloc( (size_t) (n), sizeof(t) ) // Calloc used 5657 times in 240 packages #define CallocCharBuf(n) (char *) R_chk_calloc((size_t) ((n)+1), sizeof(char)) // CallocCharBuf used 3 times in cplexAPI, patchDVI #define CreateAtVector Rf_CreateAtVector // CreateAtVector unused #define CreateTag Rf_CreateTag // CreateTag used 1 times in rgp #define DATAPTR(x) (((SEXPREC_ALIGN *) (x)) + 1) // DATAPTR used 113 times in 11 packages #define DDVAL(x) ((x)->sxpinfo.gp & 1) // DDVAL unused #define DDVAL_MASK 1 // DDVAL_MASK unused #define DECREMENT_REFCNT(x) do {} while(0) // DECREMENT_REFCNT unused #define DISABLE_REFCNT(x) do {} while(0) // DISABLE_REFCNT unused #define DOTSXP 17 // DOTSXP used 16 times in RPostgreSQL, PythonInR, Rcpp11, seqminer, Rcpp, pryr, rtkpp, spikeSlabGAM, rtkore #define DOUBLE_DATA(x) (((double *) (((SEXPREC_ALIGN *) (x)) + 1))) // DOUBLE_DATA used 9 times in bigalgebra #define DOUBLE_DIGITS 53 // DOUBLE_DIGITS used 42 times in evd #define DOUBLE_EPS 2.2204460492503131e-16 // DOUBLE_EPS used 180 times in 40 packages #define DOUBLE_XMAX 1.7976931348623157e+308 // DOUBLE_XMAX used 63 times in 13 packages #define DOUBLE_XMIN 2.2250738585072014e-308 // DOUBLE_XMIN used 13 times in unmarked, deSolve, ifultools, spatstat #define DropDims Rf_DropDims // DropDims unused #define ENABLE_NLS 1 // ENABLE_NLS used 80 times in 59 packages #define ENABLE_REFCNT(x) do {} while(0) // ENABLE_REFCNT unused #define ENCLOS(x) ((x)->u.envsxp.enclos) // ENCLOS used 7 times in Rcpp, pryr, rJava, Rcpp11, RGtk2 #define END_SUSPEND_INTERRUPTS R_interrupts_suspended = __oldsusp__; if (R_interrupts_pending && ! R_interrupts_suspended) Rf_onintr(); } while(0) // END_SUSPEND_INTERRUPTS used 18 times in 12 packages #define ENVFLAGS(x) ((x)->sxpinfo.gp) // ENVFLAGS unused #define ENVSXP 4 // ENVSXP used 63 times in 25 packages #define ERROR <defined> // ERROR used 6406 times in 293 packages #define EVAL(x) Rf_eval(x,R_GlobalEnv) // EVAL used 108 times in 13 packages #define EXPRSXP 20 // EXPRSXP used 84 times in 14 packages #define EXTPTRSXP 22 // EXTPTRSXP used 386 times in 55 packages #define EXTPTR_PROT(x) ((x)->u.listsxp.cdrval) // EXTPTR_PROT used 5 times in rJava, pryr #define EXTPTR_PTR(x) ((x)->u.listsxp.carval) // EXTPTR_PTR used 428 times in 15 packages #define EXTPTR_TAG(x) ((x)->u.listsxp.tagval) // EXTPTR_TAG used 9 times in excel.link, pryr, rJava, gsl #define EncodeComplex Rf_EncodeComplex // EncodeComplex unused #define EncodeInteger Rf_EncodeInteger // EncodeInteger used 2 times in qtbase, RGtk2 #define EncodeLogical Rf_EncodeLogical // EncodeLogical used 2 times in qtbase, RGtk2 #define EncodeReal Rf_EncodeReal // EncodeReal used 2 times in qtbase, RGtk2 #define EncodeReal0 Rf_EncodeReal0 // EncodeReal0 unused #define F77_CALL(x) x_ // F77_CALL used 4269 times in 195 packages #define F77_COM(x) x_ // F77_COM used 2 times in igraph #define F77_COMDECL(x) x_ // F77_COMDECL used 2 times in igraph #define F77_NAME(x) x_ // F77_NAME used 1913 times in 117 packages #define F77_SUB(x) x_ // F77_SUB used 771 times in 89 packages #define FALSE 0 // FALSE used 17931 times in 545 packages #define FORMALS(x) ((x)->u.closxp.formals) // FORMALS used 15 times in qtpaint, RSclient, PBSddesolve, Rserve, covr, pryr, rgp, testthat, RandomFields #define FRAME(x) ((x)->u.envsxp.frame) // FRAME used 19 times in deTestSet, IRISSeismic, pryr, BayesBridge, datamap, BayesLogit #define FREESXP 31 // FREESXP used 4 times in rtkpp, rtkore #define FUNSXP 99 // FUNSXP used 6 times in dplyr, rtkpp, data.table, rtkore #define Free(p) (R_chk_free( (void *)(p) ), (p) = __null) // Free used 21329 times in 683 packages #define GAxisPars Rf_GAxisPars // GAxisPars unused #define GETX11IMAGE_H_ // GETX11IMAGE_H_ unused #define GET_ATTR(x,what) Rf_getAttrib(x, what) // GET_ATTR used 66 times in kergp, rggobi, XML, maptools, dbarts, RGtk2 #define GET_CLASS(x) Rf_getAttrib(x, R_ClassSymbol) // GET_CLASS used 56 times in 17 packages #define GET_COLNAMES(x) Rf_GetColNames(x) // GET_COLNAMES used 14 times in multic, pomp #define GET_DIM(x) Rf_getAttrib(x, R_DimSymbol) // GET_DIM used 421 times in 55 packages #define GET_DIMNAMES(x) Rf_getAttrib(x, R_DimNamesSymbol) // GET_DIMNAMES used 60 times in multic, lfe, pomp, adaptivetau #define GET_LENGTH(x) Rf_length(x) // GET_LENGTH used 1265 times in 28 packages #define GET_LEVELS(x) Rf_getAttrib(x, R_LevelsSymbol) // GET_LEVELS used 13 times in rjson, cba, yaml #define GET_NAMES(x) Rf_getAttrib(x, R_NamesSymbol) // GET_NAMES used 84 times in 22 packages #define GET_ROWNAMES(x) Rf_GetRowNames(x) // GET_ROWNAMES used 46 times in multic, pomp, RSQLite #define GET_SLOT(x, what) R_do_slot(x, what) // GET_SLOT used 1680 times in 42 packages #define GET_TSP(x) Rf_getAttrib(x, R_TspSymbol) // GET_TSP unused #define GetArrayDimnames Rf_GetArrayDimnames // GetArrayDimnames unused #define GetColNames Rf_GetColNames // GetColNames unused #define GetMatrixDimnames Rf_GetMatrixDimnames // GetMatrixDimnames used 2 times in Kmisc, optmatch #define GetOption Rf_GetOption // GetOption used 5 times in rgl, gmp, Cairo, RGtk2 #define GetOption1 Rf_GetOption1 // GetOption1 used 1 times in PCICt #define GetOptionDigits Rf_GetOptionDigits // GetOptionDigits unused #define GetOptionWidth Rf_GetOptionWidth // GetOptionWidth unused #define GetRowNames Rf_GetRowNames // GetRowNames unused #define HASHTAB(x) ((x)->u.envsxp.hashtab) // HASHTAB used 12 times in Rcpp, pryr, datamap, Rcpp11, qtbase #define HAVE_ALLOCA_H 1 // HAVE_ALLOCA_H used 15 times in treatSens, Matrix, TMB, pbdZMQ, ore, dbarts #define HAVE_AQUA 1 // HAVE_AQUA used 13 times in 11 packages #define HAVE_EXPM1 1 // HAVE_EXPM1 used 4 times in igraph, Rcpp, BiasedUrn, Rcpp11 #define HAVE_F77_UNDERSCORE 1 // HAVE_F77_UNDERSCORE used 2 times in igraph #define HAVE_HYPOT 1 // HAVE_HYPOT used 6 times in BH, Rcpp, Rcpp11 #define HAVE_LOG1P 1 // HAVE_LOG1P used 3 times in igraph, Rcpp, Rcpp11 #define HAVE_WORKING_LOG1P 1 // HAVE_WORKING_LOG1P unused #define IEEE_754 1 // IEEE_754 used 47 times in igraph, Rcpp, data.table, stringi #define INCREMENT_NAMED(x) do { SEXP __x__ = (x); if (((__x__)->sxpinfo.named) != 2) (((__x__)->sxpinfo.named)=(((__x__)->sxpinfo.named) + 1)); } while (0) // INCREMENT_NAMED unused #define INCREMENT_REFCNT(x) do {} while(0) // INCREMENT_REFCNT unused #define INLINE_PROTECT // INLINE_PROTECT unused #define INTEGER(x) ((int *) (((SEXPREC_ALIGN *) (x)) + 1)) // INTEGER used 41659 times in 758 packages #define INTEGER_DATA(x) (((int *) (((SEXPREC_ALIGN *) (x)) + 1))) // INTEGER_DATA used 246 times in RPostgreSQL, excel.link, rggobi, XML, biganalytics, RTextTools, bcp, RGtk2 #define INTEGER_POINTER(x) ((int *) (((SEXPREC_ALIGN *) (x)) + 1)) // INTEGER_POINTER used 2082 times in 83 packages #define INTEGER_VALUE(x) Rf_asInteger(x) // INTEGER_VALUE used 451 times in 47 packages #define INTERNAL(x) ((x)->u.symsxp.internal) // INTERNAL used 1014 times in 63 packages #define INTSXP 13 // INTSXP used 6341 times in 471 packages #define ISNA(x) R_IsNA(x) // ISNA used 649 times in 100 packages #define ISNAN(x) R_isnancpp(x) // ISNAN used 1342 times in 146 packages #define IS_CHARACTER(x) (((x)->sxpinfo.type) == 16) // IS_CHARACTER used 45 times in 16 packages #define IS_COMPLEX(x) (((x)->sxpinfo.type) == 15) // IS_COMPLEX used 2 times in rjson, spsurvey #define IS_GETTER_CALL(call) (((((call)->u.listsxp.cdrval))->u.listsxp.carval) == R_TmpvalSymbol) // IS_GETTER_CALL unused #define IS_INTEGER(x) Rf_isInteger(x) // IS_INTEGER used 61 times in 19 packages #define IS_LIST(x) Rf_isVector(x) // IS_LIST used 12 times in RPostgreSQL, Runuran, XML, PythonInR, ROracle #define IS_LOGICAL(x) (((x)->sxpinfo.type) == 10) // IS_LOGICAL used 28 times in 12 packages #define IS_LONG_VEC(x) ((((VECSEXP) (x))->vecsxp.length) == -1) // IS_LONG_VEC used 1 times in RProtoBuf #define IS_NUMERIC(x) (((x)->sxpinfo.type) == 14) // IS_NUMERIC used 57 times in 17 packages #define IS_RAW(x) (((x)->sxpinfo.type) == 24) // IS_RAW used 3 times in digest, ROracle #define IS_S4_OBJECT(x) ((x)->sxpinfo.gp & ((unsigned short)(1<<4))) // IS_S4_OBJECT used 23 times in Rmosek, Runuran, data.table, xts, Matrix, slam, zoo, HiPLARM, OpenMx, tau #define IS_SCALAR(x, type) (((x)->sxpinfo.type) == (type) && (((VECSEXP) (x))->vecsxp.length) == 1) // IS_SCALAR unused #define IS_SIMPLE_SCALAR(x, type) ((((x)->sxpinfo.type) == (type) && (((VECSEXP) (x))->vecsxp.length) == 1) && ((x)->attrib) == R_NilValue) // IS_SIMPLE_SCALAR unused #define IS_VECTOR(x) Rf_isVector(x) // IS_VECTOR used 20 times in igraph, sprint, rggobi, catnet, RGtk2, sdnet #define IndexWidth Rf_IndexWidth // IndexWidth unused #define LANGSXP 6 // LANGSXP used 1276 times in 53 packages #define LCONS(a, b) Rf_lcons((a), (b)) // LCONS used 212 times in 24 packages #define LENGTH(x) (((((VECSEXP) (x))->vecsxp.length) == -1) ? R_BadLongVector(x, \"/var/folders/t8/1ry582nx6438y8pn6gk20f3c0000gn/T/preprocessor_test2759381993482855372.cpp\", 496) : (((VECSEXP) (x))->vecsxp.length)) // LENGTH used 5845 times in 356 packages #define LEVELS(x) ((x)->sxpinfo.gp) // LEVELS used 18 times in rtdists, rPref, BsMD, data.table, stringi, dplyr, OBsMD, pbdZMQ, astrochron, RandomFields #define LGLSXP 10 // LGLSXP used 1430 times in 166 packages #define LISTSXP 2 // LISTSXP used 87 times in 21 packages #define LISTVAL(x) ((x)->u.listsxp) // LISTVAL unused #define LIST_POINTER(x) ((SEXP *) (((SEXPREC_ALIGN *) (x)) + 1)) // LIST_POINTER used 15 times in RPostgreSQL, rggobi, XML, AdaptFitOS, locfit, RMySQL, RGtk2 #define LIST_VALUE(x) Rf_error(\"the 'value' of a list object is not defined\") // LIST_VALUE unused #define LOCAL_EVALUATOR // LOCAL_EVALUATOR used 11 times in rggobi, XML, ifultools, RGtk2 #define LOGICAL(x) ((int *) (((SEXPREC_ALIGN *) (x)) + 1)) // LOGICAL used 4473 times in 288 packages #define LOGICAL_DATA(x) (((int *) (((SEXPREC_ALIGN *) (x)) + 1))) // LOGICAL_DATA used 114 times in excel.link, rggobi, XML, redland, RSNNS, kza, lazy, NMF, littler, RGtk2 #define LOGICAL_POINTER(x) ((int *) (((SEXPREC_ALIGN *) (x)) + 1)) // LOGICAL_POINTER used 144 times in 15 packages #define LOGICAL_VALUE(x) Rf_asLogical(x) // LOGICAL_VALUE used 110 times in rphast, rtfbs, bigalgebra, subplex, GenABEL #define LONG_VECTOR_SUPPORT // LONG_VECTOR_SUPPORT used 56 times in stringdist, matrixStats, RApiSerialize, Rhpc, pbdMPI, Rcpp11, Matrix #define LONG_VEC_LENGTH(x) ((R_long_vec_hdr_t *) (x))[-1].lv_length // LONG_VEC_LENGTH used 1 times in Rcpp11 #define LONG_VEC_TRUELENGTH(x) ((R_long_vec_hdr_t *) (x))[-1].lv_truelength // LONG_VEC_TRUELENGTH unused #define LOOP_WITH_INTERRUPT_CHECK(LOOP, ncheck, n, ...) do { for (size_t __intr_threshold__ = ncheck; 1; __intr_threshold__ += ncheck) { size_t __intr_end__ = n < __intr_threshold__ ? n : __intr_threshold__; LOOP(__intr_end__, ...); if (__intr_end__ == n) break; else R_CheckUserInterrupt(); } } while (0) // LOOP_WITH_INTERRUPT_CHECK unused #define LTY_BLANK -1 // LTY_BLANK used 6 times in RSvgDevice, R2SWF, rvg, svglite #define LTY_DASHED 4 + (4<<4) // LTY_DASHED used 4 times in qtutils, devEMF, RSvgDevice, rvg #define LTY_DOTDASH 1 + (3<<4) + (4<<8) + (3<<12) // LTY_DOTDASH used 3 times in qtutils, devEMF, RSvgDevice #define LTY_DOTTED 1 + (3<<4) // LTY_DOTTED used 4 times in qtutils, devEMF, RSvgDevice, rvg #define LTY_LONGDASH 7 + (3<<4) // LTY_LONGDASH used 4 times in qtutils, devEMF, RSvgDevice, rvg #define LTY_SOLID 0 // LTY_SOLID used 15 times in qtutils, devEMF, rscproxy, cairoDevice, Cairo, RSvgDevice, R2SWF, rvg, JavaGD, svglite #define LTY_TWODASH 2 + (2<<4) + (6<<8) + (2<<12) // LTY_TWODASH used 2 times in qtutils, RSvgDevice #define La_extern extern // La_extern unused #define LibExport // LibExport used 2 times in hsmm #define LibExtern extern // LibExtern used 4 times in rJava #define LibImport // LibImport unused #define MAKE_CLASS(what) R_do_MAKE_CLASS(what) // MAKE_CLASS used 231 times in 29 packages #define MARK(x) ((x)->sxpinfo.mark) // MARK used 251 times in 21 packages #define MARK_NOT_MUTABLE(x) (((x)->sxpinfo.named)=(2)) // MARK_NOT_MUTABLE unused #define MAX_GRAPHICS_SYSTEMS 256 // MAX_GRAPHICS_SYSTEMS unused #define MAX_NUM_SEXPTYPE (1<<5) // MAX_NUM_SEXPTYPE unused #define MAYBE_REFERENCED(x) (! (((x)->sxpinfo.named) == 0)) // MAYBE_REFERENCED unused #define MAYBE_SHARED(x) (((x)->sxpinfo.named) > 1) // MAYBE_SHARED unused #define MESSAGE <defined> // MESSAGE used 172 times in 33 packages #define MISSING(x) ((x)->sxpinfo.gp & 15) // MISSING used 125 times in 25 packages #define MISSING_MASK 15 // MISSING_MASK used 10 times in rJPSGCS #define MOD_ITERATE(n, n1, n2, i, i1, i2, loop_body) do { i = i1 = i2 = 0; do { for (; i < n; i1 = (++i1 == n1) ? 0 : i1, i2 = (++i2 == n2) ? 0 : i2, ++i) { loop_body } } while (0); } while (0) // MOD_ITERATE unused #define MOD_ITERATE3(n, n1, n2, n3, i, i1, i2, i3, loop_body) do { i = i1 = i2 = i3 = 0; do { for (; i < n; i1 = (++i1 == n1) ? 0 : i1, i2 = (++i2 == n2) ? 0 : i2, i3 = (++i3 == n3) ? 0 : i3, ++i) { loop_body } } while (0); } while (0) // MOD_ITERATE3 unused #define MOD_ITERATE3_CHECK(ncheck, n, n1, n2, n3, i, i1, i2, i3, loop_body) do { i = i1 = i2 = i3 = 0; do { for (size_t __intr_threshold__ = ncheck; 1; __intr_threshold__ += ncheck) { size_t __intr_end__ = n < __intr_threshold__ ? n : __intr_threshold__; do { for (; i < __intr_end__; i1 = (++i1 == n1) ? 0 : i1, i2 = (++i2 == n2) ? 0 : i2, i3 = (++i3 == n3) ? 0 : i3, ++i) { loop_body } } while (0); if (__intr_end__ == n) break; else R_CheckUserInterrupt(); } } while (0); } while (0) // MOD_ITERATE3_CHECK unused #define MOD_ITERATE3_CORE(n, n1, n2, n3, i, i1, i2, i3, loop_body) do { for (; i < n; i1 = (++i1 == n1) ? 0 : i1, i2 = (++i2 == n2) ? 0 : i2, i3 = (++i3 == n3) ? 0 : i3, ++i) { loop_body } } while (0) // MOD_ITERATE3_CORE unused #define MOD_ITERATE4(n, n1, n2, n3, n4, i, i1, i2, i3, i4, loop_body) do { i = i1 = i2 = i3 = i4 = 0; do { for (; i < n; i1 = (++i1 == n1) ? 0 : i1, i2 = (++i2 == n2) ? 0 : i2, i3 = (++i3 == n3) ? 0 : i3, i4 = (++i4 == n4) ? 0 : i4, ++i) { loop_body } } while (0); } while (0) // MOD_ITERATE4 unused #define MOD_ITERATE4_CHECK(ncheck, n, n1, n2, n3, n4, i, i1, i2, i3, i4, loop_body) do { i = i1 = i2 = i3 = i4 = 0; do { for (size_t __intr_threshold__ = ncheck; 1; __intr_threshold__ += ncheck) { size_t __intr_end__ = n < __intr_threshold__ ? n : __intr_threshold__; do { for (; i < __intr_end__; i1 = (++i1 == n1) ? 0 : i1, i2 = (++i2 == n2) ? 0 : i2, i3 = (++i3 == n3) ? 0 : i3, i4 = (++i4 == n4) ? 0 : i4, ++i) { loop_body } } while (0); if (__intr_end__ == n) break; else R_CheckUserInterrupt(); } } while (0); } while (0) // MOD_ITERATE4_CHECK unused #define MOD_ITERATE4_CORE(n, n1, n2, n3, n4, i, i1, i2, i3, i4, loop_body) do { for (; i < n; i1 = (++i1 == n1) ? 0 : i1, i2 = (++i2 == n2) ? 0 : i2, i3 = (++i3 == n3) ? 0 : i3, i4 = (++i4 == n4) ? 0 : i4, ++i) { loop_body } } while (0) // MOD_ITERATE4_CORE unused #define MOD_ITERATE5(n, n1, n2, n3, n4, n5, i, i1, i2, i3, i4, i5, loop_body) do { i = i1 = i2 = i3 = i4 = i5 = 0; do { for (; i < n; i1 = (++i1 == n1) ? 0 : i1, i2 = (++i2 == n2) ? 0 : i2, i3 = (++i3 == n3) ? 0 : i3, i4 = (++i4 == n4) ? 0 : i4, i5 = (++i5 == n5) ? 0 : i5, ++i) { loop_body } } while (0); } while (0) // MOD_ITERATE5 unused #define MOD_ITERATE5_CHECK(ncheck, n, n1, n2, n3, n4, n5, i, i1, i2, i3, i4, i5, loop_body) do { i = i1 = i2 = i3 = i4 = i5 = 0; do { for (size_t __intr_threshold__ = ncheck; 1; __intr_threshold__ += ncheck) { size_t __intr_end__ = n < __intr_threshold__ ? n : __intr_threshold__; do { for (; i < __intr_end__; i1 = (++i1 == n1) ? 0 : i1, i2 = (++i2 == n2) ? 0 : i2, i3 = (++i3 == n3) ? 0 : i3, i4 = (++i4 == n4) ? 0 : i4, i5 = (++i5 == n5) ? 0 : i5, ++i) { loop_body } } while (0); if (__intr_end__ == n) break; else R_CheckUserInterrupt(); } } while (0); } while (0) // MOD_ITERATE5_CHECK unused #define MOD_ITERATE5_CORE(n, n1, n2, n3, n4, n5, i, i1, i2, i3, i4, i5, loop_body) do { for (; i < n; i1 = (++i1 == n1) ? 0 : i1, i2 = (++i2 == n2) ? 0 : i2, i3 = (++i3 == n3) ? 0 : i3, i4 = (++i4 == n4) ? 0 : i4, i5 = (++i5 == n5) ? 0 : i5, ++i) { loop_body } } while (0) // MOD_ITERATE5_CORE unused #define MOD_ITERATE_CHECK(ncheck, n, n1, n2, i, i1, i2, loop_body) do { i = i1 = i2 = 0; do { for (size_t __intr_threshold__ = ncheck; 1; __intr_threshold__ += ncheck) { size_t __intr_end__ = n < __intr_threshold__ ? n : __intr_threshold__; do { for (; i < __intr_end__; i1 = (++i1 == n1) ? 0 : i1, i2 = (++i2 == n2) ? 0 : i2, ++i) { loop_body } } while (0); if (__intr_end__ == n) break; else R_CheckUserInterrupt(); } } while (0); } while (0) // MOD_ITERATE_CHECK unused #define MOD_ITERATE_CORE(n, n1, n2, i, i1, i2, loop_body) do { for (; i < n; i1 = (++i1 == n1) ? 0 : i1, i2 = (++i2 == n2) ? 0 : i2, ++i) { loop_body } } while (0) // MOD_ITERATE_CORE unused #define M_1_PI 0.318309886183790671537767526745028724 // M_1_PI used 42 times in SpatialExtremes, decon, mvabund, geoR, geoRglm, ExomeDepth, libamtrack, miRada, RandomFields, DescTools #define M_1_SQRT_2PI 0.398942280401432677939946059934 // M_1_SQRT_2PI used 61 times in 23 packages #define M_2PI 6.283185307179586476925286766559 // M_2PI used 106 times in 16 packages #define M_2_PI 0.636619772367581343075535053490057448 // M_2_PI used 27 times in RandomFieldsUtils, dynaTree, ExomeDepth, RandomFields, svd, DescTools, spatstat #define M_2_SQRTPI 1.12837916709551257389615890312154517 // M_2_SQRTPI used 6 times in excursions, PearsonDS, SpecsVerification, ExomeDepth #define M_E 2.71828182845904523536028747135266250 // M_E used 40 times in Runuran, lamW, gmum.r, ExomeDepth, CEC, PoweR, TMB, Bmix, tgp, RcppShark #define M_LN10 2.30258509299404568401799145468436421 // M_LN10 used 27 times in monomvn, rphast, secr, Runuran, rtfbs, PlayerRatings, ExomeDepth, spaMM, logistf, laGP #define M_LN2 0.693147180559945309417232121458176568 // M_LN2 used 166 times in 30 packages #define M_LN_2PI 1.837877066409345483560659472811 // M_LN_2PI used 4 times in OpenMx, MPSEM #define M_LN_SQRT_2PI 0.918938533204672741780329736406 // M_LN_SQRT_2PI used 111 times in 31 packages #define M_LN_SQRT_PI 0.572364942924700087071713675677 // M_LN_SQRT_PI used 29 times in 12 packages #define M_LN_SQRT_PId2 0.225791352644727432363097614947 // M_LN_SQRT_PId2 used 9 times in MCMCpack, MasterBayes, phcfM, RandomFields, gof #define M_LOG10E 0.434294481903251827651128918916605082 // M_LOG10E used 2 times in ExomeDepth #define M_LOG10_2 0.301029995663981195213738894724 // M_LOG10_2 used 9 times in Bessel #define M_LOG2E 1.44269504088896340735992468100189214 // M_LOG2E used 2 times in ExomeDepth #define M_PI 3.14159265358979323846264338327950288 // M_PI used 1853 times in 207 packages #define M_PI_2 1.57079632679489661923132169163975144 // M_PI_2 used 149 times in 28 packages #define M_PI_4 0.785398163397448309615660845819875721 // M_PI_4 used 18 times in 12 packages #define M_SQRT1_2 0.707106781186547524400844362104849039 // M_SQRT1_2 used 26 times in SpatialExtremes, gmwm, excursions, forecast, subrank, dplR, ExomeDepth, SpecsVerification #define M_SQRT2 1.41421356237309504880168872420969808 // M_SQRT2 used 72 times in 23 packages #define M_SQRT_2dPI 0.797884560802865355879892119869 // M_SQRT_2dPI used 2 times in SpatialExtremes, energy #define M_SQRT_3 1.732050807568877293527446341506 // M_SQRT_3 used 4 times in poibin, SpatialExtremes, RandomFields, DescTools #define M_SQRT_32 5.656854249492380195206754896838 // M_SQRT_32 used 10 times in MCMCpack, MasterBayes, rforensicbatwing, phcfM, gof #define M_SQRT_PI 1.772453850905516027298167483341 // M_SQRT_PI used 31 times in SpatialExtremes, geoR, plugdensity, anchors, BayesBridge, copula, RandomFields, bda, DescTools #define Memcpy(p,q,n) memcpy( p, q, (size_t)(n) * sizeof(*p) ) // Memcpy used 483 times in 32 packages #define Memzero(p,n) memset(p, 0, (size_t)(n) * sizeof(*p)) // Memzero used 5 times in Matrix #define NAMED(x) ((x)->sxpinfo.named) // NAMED used 62 times in 22 packages #define NAMEDMAX 2 // NAMEDMAX unused #define NA_INTEGER R_NaInt // NA_INTEGER used 1520 times in 183 packages #define NA_LOGICAL R_NaInt // NA_LOGICAL used 355 times in 73 packages #define NA_REAL R_NaReal // NA_REAL used 1667 times in 226 packages #define NA_STRING R_NaString // NA_STRING used 574 times in 90 packages #define NEW(class_def) R_do_new_object(class_def) // NEW used 1245 times in 153 packages #define NEWSXP 30 // NEWSXP used 4 times in rtkpp, rtkore #define NEW_CHARACTER(n) Rf_allocVector(16,n) // NEW_CHARACTER used 636 times in 49 packages #define NEW_COMPLEX(n) Rf_allocVector(15,n) // NEW_COMPLEX used 3 times in igraph, ifs #define NEW_INTEGER(n) Rf_allocVector(13,n) // NEW_INTEGER used 870 times in 94 packages #define NEW_LIST(n) Rf_allocVector(19,n) // NEW_LIST used 532 times in 52 packages #define NEW_LOGICAL(n) Rf_allocVector(10,n) // NEW_LOGICAL used 157 times in 38 packages #define NEW_NUMERIC(n) Rf_allocVector(14,n) // NEW_NUMERIC used 1139 times in 112 packages #define NEW_OBJECT(class_def) R_do_new_object(class_def) // NEW_OBJECT used 218 times in 25 packages #define NEW_RAW(n) Rf_allocVector(24,n) // NEW_RAW used 9 times in RPostgreSQL, rggobi, ROracle, oce #define NEW_STRING(n) Rf_allocVector(16,n) // NEW_STRING used 38 times in 11 packages #define NILSXP 0 // NILSXP used 169 times in 44 packages #define NORET __attribute__((noreturn)) // NORET unused #define NOT_SHARED(x) (! (((x)->sxpinfo.named) > 1)) // NOT_SHARED unused #define NO_REFERENCES(x) (((x)->sxpinfo.named) == 0) // NO_REFERENCES unused #define NULL_ENTRY // NULL_ENTRY used 170 times in 12 packages #define NULL_USER_OBJECT R_NilValue // NULL_USER_OBJECT used 8268 times in rggobi, XML, rjson, bigmemory, dbarts, lazy, RGtk2 #define NUMERIC_DATA(x) (((double *) (((SEXPREC_ALIGN *) (x)) + 1))) // NUMERIC_DATA used 71 times in excel.link, rggobi, XML, biganalytics, bigalgebra, bcp, RGtk2 #define NUMERIC_POINTER(x) ((double *) (((SEXPREC_ALIGN *) (x)) + 1)) // NUMERIC_POINTER used 2527 times in 101 packages #define NUMERIC_VALUE(x) Rf_asReal(x) // NUMERIC_VALUE used 178 times in 25 packages #define NewFrameConfirm Rf_NewFrameConfirm // NewFrameConfirm unused #define NoDevices Rf_NoDevices // NoDevices used 1 times in tkrplot #define NonNullStringMatch Rf_NonNullStringMatch // NonNullStringMatch used 8 times in proxy, arules, arulesSequences, cba #define NumDevices Rf_NumDevices // NumDevices used 3 times in JavaGD #define OBJECT(x) ((x)->sxpinfo.obj) // OBJECT used 102 times in 28 packages #define PI 3.14159265358979323846264338327950288 // PI unused #define PREXPR(e) R_PromiseExpr(e) // PREXPR used 4 times in igraph, lazyeval #define PRINTNAME(x) ((x)->u.symsxp.pname) // PRINTNAME used 92 times in 29 packages #define PROBLEM <defined> // PROBLEM used 861 times in 78 packages #define PROMSXP 5 // PROMSXP used 43 times in 14 packages #define PROTECT(s) Rf_protect(s) // PROTECT used 24686 times in 767 packages #define PROTECT_WITH_INDEX(x,i) R_ProtectWithIndex(x,i) // PROTECT_WITH_INDEX used 91 times in 27 packages #define PRTUTIL_H_ // PRTUTIL_H_ unused #define PairToVectorList Rf_PairToVectorList // PairToVectorList used 7 times in cba, rcdd #define PrintValue Rf_PrintValue // PrintValue used 119 times in 13 packages #define QDFLAG_DISPLAY_LIST 0x0001 // QDFLAG_DISPLAY_LIST unused #define QDFLAG_INTERACTIVE 0x0002 // QDFLAG_INTERACTIVE unused #define QDFLAG_RASTERIZED 0x0004 // QDFLAG_RASTERIZED unused #define QNPF_REDRAW 0x0001 // QNPF_REDRAW unused #define QPFLAG_ANTIALIAS 0x0100 // QPFLAG_ANTIALIAS unused #define QP_Flags_CFLoop 0x0001 // QP_Flags_CFLoop unused #define QP_Flags_Cocoa 0x0002 // QP_Flags_Cocoa unused #define QP_Flags_Front 0x0004 // QP_Flags_Front unused #define QuartzParam_EmbeddingFlags \"embeddeding flags\" // QuartzParam_EmbeddingFlags unused #define RAW(x) ((Rbyte *) (((SEXPREC_ALIGN *) (x)) + 1)) // RAW used 880 times in 99 packages #define RAWSXP 24 // RAWSXP used 587 times in 92 packages #define RAW_POINTER(x) ((Rbyte *) (((SEXPREC_ALIGN *) (x)) + 1)) // RAW_POINTER used 31 times in RPostgreSQL, CrypticIBDcheck, rggobi, seqinr, IRISSeismic, oce, RGtk2 #define RAW_VALUE(x) Rf_error(\"the 'value' of a raw object is not defined\") // RAW_VALUE unused #define RDEBUG(x) ((x)->sxpinfo.debug) // RDEBUG used 69 times in rmetasim #define REAL(x) ((double *) (((SEXPREC_ALIGN *) (x)) + 1)) // REAL used 30947 times in 687 packages #define REALSXP 14 // REALSXP used 10171 times in 573 packages #define RECOVER <defined> // RECOVER used 170 times in 14 packages #define RECURSIVE_DATA(x) (((SEXP *) (((SEXPREC_ALIGN *) (x)) + 1))) // RECURSIVE_DATA used 5 times in XML #define REFCNT(x) 0 // REFCNT unused #define REFCNTMAX (4 - 1) // REFCNTMAX unused #define REMBEDDED_H_ // REMBEDDED_H_ unused #define REPROTECT(x,i) R_Reprotect(x,i) // REPROTECT used 130 times in 25 packages #define RGBpar Rf_RGBpar // RGBpar used 3 times in Cairo, jpeg #define RGBpar3 Rf_RGBpar3 // RGBpar3 unused #define RINTERFACE_H_ // RINTERFACE_H_ unused #define RMATH_H // RMATH_H used 1 times in phyclust #define RSTEP(x) ((x)->sxpinfo.spare) // RSTEP unused #define RTRACE(x) ((x)->sxpinfo.trace) // RTRACE unused #define R_ALLOCATOR_TYPE // R_ALLOCATOR_TYPE unused #define R_ALPHA(col) (((col)>>24)&255) // R_ALPHA used 35 times in 13 packages #define R_APPLIC_H_ // R_APPLIC_H_ unused #define R_ARITH_H_ // R_ARITH_H_ unused #define R_BLAS_H // R_BLAS_H used 2 times in slam #define R_BLUE(col) (((col)>>16)&255) // R_BLUE used 29 times in 12 packages #define R_CALLBACKS_H // R_CALLBACKS_H unused #define R_COMPLEX_H // R_COMPLEX_H used 1 times in uniqueAtomMat #define R_CONNECTIONS_VERSION 1 // R_CONNECTIONS_VERSION used 3 times in curl, iotools #define R_Calloc(n, t) (t *) R_chk_calloc( (size_t) (n), sizeof(t) ) // R_Calloc used 81 times in clpAPI, cplexAPI, poppr, rLindo, glpkAPI #define R_CheckStack() do { void __attribute__((noreturn)) R_SignalCStackOverflow(intptr_t); int dummy; intptr_t usage = R_CStackDir * (R_CStackStart - (uintptr_t)&dummy); if(R_CStackLimit != -1 && usage > ((intptr_t) R_CStackLimit)) R_SignalCStackOverflow(usage); } while (0) // R_CheckStack used 115 times in vcrpart, actuar, cplm, lme4, Matrix, GNE, randtoolbox, HiPLARM, rngWELL, pedigreemm #define R_DEFINES_H // R_DEFINES_H unused #define R_ERROR_H_ // R_ERROR_H_ unused #define R_EXT_BOOLEAN_H_ // R_EXT_BOOLEAN_H_ used 2 times in jpeg, Rcpp11 #define R_EXT_CONNECTIONS_H_ // R_EXT_CONNECTIONS_H_ unused #define R_EXT_CONSTANTS_H_ // R_EXT_CONSTANTS_H_ unused #define R_EXT_DYNLOAD_H_ // R_EXT_DYNLOAD_H_ unused #define R_EXT_EVENTLOOP_H // R_EXT_EVENTLOOP_H unused #define R_EXT_ITERMACROS_H_ // R_EXT_ITERMACROS_H_ unused #define R_EXT_MATHTHREADS_H_ // R_EXT_MATHTHREADS_H_ unused #define R_EXT_MEMORY_H_ // R_EXT_MEMORY_H_ unused #define R_EXT_PARSE_H_ // R_EXT_PARSE_H_ used 2 times in Rserve #define R_EXT_PRINT_H_ // R_EXT_PRINT_H_ used 6 times in spTDyn, spTimer #define R_EXT_QUARTZDEVICE_H_ // R_EXT_QUARTZDEVICE_H_ unused #define R_EXT_RALLOCATORS_H_ // R_EXT_RALLOCATORS_H_ unused #define R_EXT_RSTARTUP_H_ // R_EXT_RSTARTUP_H_ unused #define R_EXT_UTILS_H_ // R_EXT_UTILS_H_ unused #define R_EXT_VISIBILITY_H_ // R_EXT_VISIBILITY_H_ unused #define R_FINITE(x) R_finite(x) // R_FINITE used 1387 times in 145 packages #define R_FTP_HTTP_H_ // R_FTP_HTTP_H_ unused #define R_Free(p) (R_chk_free( (void *)(p) ), (p) = __null) // R_Free used 78 times in clpAPI, cplexAPI, poppr, glpkAPI #define R_GE_version 10 // R_GE_version used 51 times in 12 packages #define R_GRAPHICSDEVICE_H_ // R_GRAPHICSDEVICE_H_ unused #define R_GRAPHICSENGINE_H_ // R_GRAPHICSENGINE_H_ unused #define R_GREEN(col) (((col)>> 8)&255) // R_GREEN used 29 times in 12 packages #define R_ICONV_H // R_ICONV_H unused #define R_INLINE inline // R_INLINE used 330 times in 34 packages #define R_INTERNALS_H_ // R_INTERNALS_H_ used 7 times in uniqueAtomMat, rtkpp, rtkore, spatstat #define R_ITERATE(n, i, loop_body) do { i = 0; do { for (; i < n; ++i) { loop_body } } while (0); } while (0) // R_ITERATE unused #define R_ITERATE_CHECK(ncheck, n, i, loop_body) do { i = 0; do { for (size_t __intr_threshold__ = ncheck; 1; __intr_threshold__ += ncheck) { size_t __intr_end__ = n < __intr_threshold__ ? n : __intr_threshold__; do { for (; i < __intr_end__; ++i) { loop_body } } while (0); if (__intr_end__ == n) break; else R_CheckUserInterrupt(); } } while (0); } while (0) // R_ITERATE_CHECK unused #define R_ITERATE_CORE(n, i, loop_body) do { for (; i < n; ++i) { loop_body } } while (0) // R_ITERATE_CORE unused #define R_LAPACK_H // R_LAPACK_H unused #define R_LEN_T_MAX 2147483647 // R_LEN_T_MAX used 4 times in stringdist, matrixStats, FREGAT, Rcpp11 #define R_LINPACK_H_ // R_LINPACK_H_ unused #define R_LONG_VEC_TOKEN -1 // R_LONG_VEC_TOKEN used 1 times in Rcpp11 #define R_OPAQUE(col) ((((col)>>24)&255) == 255) // R_OPAQUE used 6 times in devEMF, tikzDevice, cairoDevice #define R_PROBLEM_BUFSIZE 4096 // R_PROBLEM_BUFSIZE unused #define R_RANDOM_H // R_RANDOM_H unused #define R_RCONFIG_H // R_RCONFIG_H unused #define R_RED(col) (((col) )&255) // R_RED used 37 times in 12 packages #define R_RGB(r,g,b) ((r)|((g)<<8)|((b)<<16)|0xFF000000) // R_RGB used 23 times in qtutils, rscproxy, cairoDevice, Cairo, jpeg, R2SWF, rvg, JavaGD, png, svglite #define R_RGBA(r,g,b,a) ((r)|((g)<<8)|((b)<<16)|((a)<<24)) // R_RGBA used 6 times in Cairo, jpeg, png, showtext #define R_RS_H // R_RS_H unused #define R_R_H // R_R_H used 9 times in TMB, uniqueAtomMat, DatABEL, GenABEL, VariABEL #define R_Realloc(p,n,t) (t *) R_chk_realloc( (void *)(p), (size_t)((n) * sizeof(t)) ) // R_Realloc used 3 times in poppr, seqminer, gpuR #define R_SHORT_LEN_MAX 2147483647 // R_SHORT_LEN_MAX used 1 times in pbdMPI #define R_STATS_PACKAGE_H // R_STATS_PACKAGE_H unused #define R_S_H // R_S_H unused #define R_TRANSPARENT(col) ((((col)>>24)&255) == 0) // R_TRANSPARENT used 16 times in qtutils, devEMF, tikzDevice, Cairo #define R_TRANWHITE (((255)|((255)<<8)|((255)<<16)|((0)<<24))) // R_TRANWHITE used 6 times in qtutils, devEMF, rscproxy, cairoDevice, showtext #define R_USE_PROTOTYPES 1 // R_USE_PROTOTYPES used 10 times in qtutils, rscproxy, tikzDevice, R2SWF, showtext #define R_VERSION_STRING \"3.2.4\" // R_VERSION_STRING unused #define R_XDR_DOUBLE_SIZE 8 // R_XDR_DOUBLE_SIZE used 2 times in rgdal #define R_XDR_INTEGER_SIZE 4 // R_XDR_INTEGER_SIZE used 3 times in rgdal #define R_XLEN_T_MAX 4503599627370496 // R_XLEN_T_MAX used 7 times in stringdist, Matrix, matrixStats, RApiSerialize, Rhpc #define Realloc(p,n,t) (t *) R_chk_realloc( (void *)(p), (size_t)((n) * sizeof(t)) ) // Realloc used 244 times in 57 packages #define S3Class Rf_S3Class // S3Class used 4 times in RInside, littler #define S4SXP 25 // S4SXP used 71 times in 15 packages #define S4_OBJECT_MASK ((unsigned short)(1<<4)) // S4_OBJECT_MASK unused #define SETLENGTH(x,v) do { SEXP sl__x__ = (x); R_xlen_t sl__v__ = (v); if (((((VECSEXP) (sl__x__))->vecsxp.length) == -1)) (((R_long_vec_hdr_t *) (sl__x__))[-1].lv_length = (sl__v__)); else ((((VECSEXP) (sl__x__))->vecsxp.length) = ((R_len_t) sl__v__)); } while (0) // SETLENGTH used 65 times in 11 packages #define SETLEVELS(x,v) (((x)->sxpinfo.gp)=((unsigned short)v)) // SETLEVELS used 2 times in Rcpp11 #define SET_ATTR(x, what, n) Rf_setAttrib(x, what, n) // SET_ATTR used 12 times in rphast, kergp, rtfbs, TPmsm, dbarts, PBSmapping #define SET_CLASS(x, n) Rf_setAttrib(x, R_ClassSymbol, n) // SET_CLASS used 120 times in 19 packages #define SET_DDVAL(x,v) ((v) ? (((x)->sxpinfo.gp) |= 1) : (((x)->sxpinfo.gp) &= ~1)) // SET_DDVAL unused #define SET_DDVAL_BIT(x) (((x)->sxpinfo.gp) |= 1) // SET_DDVAL_BIT unused #define SET_DIM(x, n) Rf_setAttrib(x, R_DimSymbol, n) // SET_DIM used 54 times in 18 packages #define SET_DIMNAMES(x, n) Rf_setAttrib(x, R_DimNamesSymbol, n) // SET_DIMNAMES used 17 times in multic, lfe, pomp, subplex, TPmsm, cba #define SET_ELEMENT(x, i, val) SET_VECTOR_ELT(x, i, val) // SET_ELEMENT used 344 times in 18 packages #define SET_ENVFLAGS(x,v) (((x)->sxpinfo.gp)=(v)) // SET_ENVFLAGS unused #define SET_LENGTH(x, n) (x = Rf_lengthgets(x, n)) // SET_LENGTH used 45 times in 12 packages #define SET_LEVELS(x, l) Rf_setAttrib(x, R_LevelsSymbol, l) // SET_LEVELS used 9 times in cba, rggobi #define SET_LONG_VEC_LENGTH(x,v) (((R_long_vec_hdr_t *) (x))[-1].lv_length = (v)) // SET_LONG_VEC_LENGTH unused #define SET_LONG_VEC_TRUELENGTH(x,v) (((R_long_vec_hdr_t *) (x))[-1].lv_truelength = (v)) // SET_LONG_VEC_TRUELENGTH unused #define SET_MISSING(x,v) do { SEXP __x__ = (x); int __v__ = (v); int __other_flags__ = __x__->sxpinfo.gp & ~15; __x__->sxpinfo.gp = __other_flags__ | __v__; } while (0) // SET_MISSING used 1 times in sprint #define SET_NAMED(x, v) (((x)->sxpinfo.named)=(v)) // SET_NAMED used 10 times in dplyr, yaml, data.table, iotools, RSQLite #define SET_NAMES(x, n) Rf_setAttrib(x, R_NamesSymbol, n) // SET_NAMES used 346 times in 37 packages #define SET_OBJECT(x,v) (((x)->sxpinfo.obj)=(v)) // SET_OBJECT used 32 times in RSclient, reshape2, Rserve, data.table, actuar, dplyr, proxy, rmongodb, slam, tau #define SET_RDEBUG(x,v) (((x)->sxpinfo.debug)=(v)) // SET_RDEBUG unused #define SET_REFCNT(x,v) do {} while(0) // SET_REFCNT unused #define SET_RSTEP(x,v) (((x)->sxpinfo.spare)=(v)) // SET_RSTEP unused #define SET_RTRACE(x,v) (((x)->sxpinfo.trace)=(v)) // SET_RTRACE unused #define SET_S4_OBJECT(x) (((x)->sxpinfo.gp) |= ((unsigned short)(1<<4))) // SET_S4_OBJECT used 12 times in RSclient, redland, Rserve, data.table, FREGAT, rJPSGCS, tau #define SET_SHORT_VEC_LENGTH SET_SHORT_VEC_LENGTH // SET_SHORT_VEC_LENGTH unused #define SET_SHORT_VEC_TRUELENGTH SET_SHORT_VEC_TRUELENGTH // SET_SHORT_VEC_TRUELENGTH unused #define SET_SLOT(x, what, value) R_do_slot_assign(x, what, value) // SET_SLOT used 561 times in 32 packages #define SET_TRACKREFS(x,v) do {} while(0) // SET_TRACKREFS unused #define SET_TRUELENGTH(x,v) do { SEXP sl__x__ = (x); R_xlen_t sl__v__ = (v); if (((((VECSEXP) (sl__x__))->vecsxp.length) == -1)) (((R_long_vec_hdr_t *) (sl__x__))[-1].lv_truelength = (sl__v__)); else ((((VECSEXP) (sl__x__))->vecsxp.truelength) = ((R_len_t) sl__v__)); } while (0) // SET_TRUELENGTH used 26 times in data.table #define SET_TYPEOF(x,v) (((x)->sxpinfo.type)=(v)) // SET_TYPEOF used 38 times in 21 packages #define SEXPREC_HEADER <defined> // SEXPREC_HEADER unused #define SHORT_VEC_LENGTH(x) (((VECSEXP) (x))->vecsxp.length) // SHORT_VEC_LENGTH used 1 times in Rcpp11 #define SHORT_VEC_TRUELENGTH(x) (((VECSEXP) (x))->vecsxp.truelength) // SHORT_VEC_TRUELENGTH unused #define SINGLESXP 302 // SINGLESXP used 1 times in rgl #define SINGLE_BASE 2 // SINGLE_BASE unused #define SINGLE_EPS 1.19209290e-7F // SINGLE_EPS unused #define SINGLE_XMAX 3.40282347e+38F // SINGLE_XMAX used 4 times in mapproj #define SINGLE_XMIN 1.17549435e-38F // SINGLE_XMIN unused #define SINT_MAX 2147483647 // SINT_MAX used 4 times in robust, AnalyzeFMRI #define SINT_MIN (-2147483647 -1) // SINT_MIN used 2 times in robust #define SIZEOF_SIZE_T 8 // SIZEOF_SIZE_T used 1 times in PythonInR #define SPECIALSXP 7 // SPECIALSXP used 22 times in RPostgreSQL, PythonInR, Rcpp11, purrr, seqminer, Rcpp, yaml, pryr, rtkpp, rtkore #define STRING_ELT(x,i) ((SEXP *) (((SEXPREC_ALIGN *) (x)) + 1))[i] // STRING_ELT used 4143 times in 333 packages #define STRING_PTR(x) ((SEXP *) (((SEXPREC_ALIGN *) (x)) + 1)) // STRING_PTR used 65 times in 14 packages #define STRING_VALUE(x) ((const char *) (((SEXPREC_ALIGN *) (Rf_asChar(x))) + 1)) // STRING_VALUE used 13 times in rggobi, XML, rgenoud, ParamHelpers, digest, lazy, RGtk2, SoDA, spatstat #define STRSXP 16 // STRSXP used 3247 times in 327 packages #define SUPPORT_MBCS 1 // SUPPORT_MBCS used 1 times in bibtex #define SUPPORT_UTF8 1 // SUPPORT_UTF8 used 3 times in tau, rindex, stringi #define SYMSXP 1 // SYMSXP used 94 times in 25 packages #define SYMVALUE(x) ((x)->u.symsxp.value) // SYMVALUE unused #define S_EVALUATOR // S_EVALUATOR used 66 times in 13 packages #define Salloc(n,t) (t*)S_alloc(n, sizeof(t)) // Salloc used 299 times in logspline, multic, polspline, splusTimeDate, geoRglm, haplo.stats, tree, ibdreg, IDPmisc, robust #define ScalarComplex Rf_ScalarComplex // ScalarComplex unused #define ScalarInteger Rf_ScalarInteger // ScalarInteger used 704 times in 88 packages #define ScalarLogical Rf_ScalarLogical // ScalarLogical used 450 times in 64 packages #define ScalarRaw Rf_ScalarRaw // ScalarRaw used 4 times in qtbase, RGtk2 #define ScalarReal Rf_ScalarReal // ScalarReal used 330 times in 65 packages #define ScalarString Rf_ScalarString // ScalarString used 198 times in 37 packages #define Srealloc(p,n,old,t) (t*)S_realloc(p,n,old,sizeof(t)) // Srealloc unused #define StdinActivity 2 // StdinActivity unused #define StringBlank Rf_StringBlank // StringBlank unused #define StringFalse Rf_StringFalse // StringFalse used 3 times in iotools #define StringTrue Rf_StringTrue // StringTrue used 3 times in iotools #define TAG(e) ((e)->u.listsxp.tagval) // TAG used 513 times in 40 packages #define TRACKREFS(x) 0 // TRACKREFS unused #define TRUE 1 // TRUE used 17978 times in 575 packages #define TRUELENGTH(x) (((((VECSEXP) (x))->vecsxp.length) == -1) ? R_BadLongVector(x, \"/var/folders/t8/1ry582nx6438y8pn6gk20f3c0000gn/T/preprocessor_test2759381993482855372.cpp\", 1384) : (((VECSEXP) (x))->vecsxp.truelength)) // TRUELENGTH used 37 times in data.table #define TYPEOF(x) ((x)->sxpinfo.type) // TYPEOF used 2832 times in 195 packages #define TYPE_BITS 5 // TYPE_BITS used 2 times in dplyr #define UNPROTECT(n) Rf_unprotect(n) // UNPROTECT used 12247 times in 758 packages #define UNPROTECT_PTR(s) Rf_unprotect_ptr(s) // UNPROTECT_PTR used 307 times in 14 packages #define UNSET_DDVAL_BIT(x) (((x)->sxpinfo.gp) &= ~1) // UNSET_DDVAL_BIT unused #define UNSET_S4_OBJECT(x) (((x)->sxpinfo.gp) &= ~((unsigned short)(1<<4))) // UNSET_S4_OBJECT used 2 times in data.table, slam #define USING_R // USING_R used 238 times in 29 packages #define VECSXP 19 // VECSXP used 3142 times in 385 packages #define VECTOR_DATA(x) (((SEXP *) (((SEXPREC_ALIGN *) (x)) + 1))) // VECTOR_DATA unused #define VECTOR_ELT(x,i) ((SEXP *) (((SEXPREC_ALIGN *) (x)) + 1))[i] // VECTOR_ELT used 8626 times in 291 packages #define VECTOR_PTR(x) ((SEXP *) (((SEXPREC_ALIGN *) (x)) + 1)) // VECTOR_PTR used 17 times in bit, AdaptFitOS, RJSONIO, Rcpp11, bit64, Rcpp, locfit, iotools #define VectorIndex Rf_VectorIndex // VectorIndex used 6 times in gnmf #define VectorToPairList Rf_VectorToPairList // VectorToPairList used 13 times in pomp, arules #define WARN <defined> // WARN used 122 times in 20 packages #define WARNING <defined> // WARNING used 957 times in 190 packages #define WEAKREFSXP 23 // WEAKREFSXP used 19 times in seqminer, Rcpp, pryr, rtkpp, rtkore, Rcpp11 #define XActivity 1 // XActivity used 1 times in rgl #define XLENGTH(x) (((((VECSEXP) (x))->vecsxp.length) == -1) ? ((R_long_vec_hdr_t *) (x))[-1].lv_length : (((VECSEXP) (x))->vecsxp.length)) // XLENGTH used 287 times in 21 packages #define XTRUELENGTH(x) (((((VECSEXP) (x))->vecsxp.length) == -1) ? ((R_long_vec_hdr_t *) (x))[-1].lv_truelength : (((VECSEXP) (x))->vecsxp.truelength)) // XTRUELENGTH unused #define __STDC_WANT_IEC_60559_FUNCS_EXT__ 1 // __STDC_WANT_IEC_60559_FUNCS_EXT__ unused #define acopy_string Rf_acopy_string // acopy_string used 10 times in splusTimeDate #define addMissingVarsToNewEnv Rf_addMissingVarsToNewEnv // addMissingVarsToNewEnv unused #define alloc3DArray Rf_alloc3DArray // alloc3DArray used 21 times in mcmc, msm, TPmsm, unfoldr, RandomFields, cplm #define allocArray Rf_allocArray // allocArray used 24 times in unfoldr, kergp, pomp, proxy, kza, slam, mvMORPH, TPmsm, ouch, RandomFields #define allocFormalsList2 Rf_allocFormalsList2 // allocFormalsList2 unused #define allocFormalsList3 Rf_allocFormalsList3 // allocFormalsList3 unused #define allocFormalsList4 Rf_allocFormalsList4 // allocFormalsList4 unused #define allocFormalsList5 Rf_allocFormalsList5 // allocFormalsList5 unused #define allocFormalsList6 Rf_allocFormalsList6 // allocFormalsList6 unused #define allocList Rf_allocList // allocList used 60 times in 25 packages #define allocMatrix Rf_allocMatrix // allocMatrix used 1577 times in 244 packages #define allocS4Object Rf_allocS4Object // allocS4Object used 1 times in arules #define allocSExp Rf_allocSExp // allocSExp used 14 times in igraph, rgp, data.table, RandomFields, mmap, qtbase #define allocVector Rf_allocVector // allocVector used 12419 times in 551 packages #define allocVector3 Rf_allocVector3 // allocVector3 unused #define any_duplicated Rf_any_duplicated // any_duplicated used 5 times in data.table, checkmate #define any_duplicated3 Rf_any_duplicated3 // any_duplicated3 unused #define applyClosure Rf_applyClosure // applyClosure unused #define arraySubscript Rf_arraySubscript // arraySubscript used 13 times in proxy, arules, arulesSequences, cba, seriation #define asChar Rf_asChar // asChar used 194 times in 36 packages #define asCharacterFactor Rf_asCharacterFactor // asCharacterFactor used 11 times in fastmatch, Kmisc, data.table #define asComplex Rf_asComplex // asComplex used 1 times in ff #define asInteger Rf_asInteger // asInteger used 1277 times in 140 packages #define asLogical Rf_asLogical // asLogical used 462 times in 64 packages #define asReal Rf_asReal // asReal used 383 times in 83 packages #define asS4 Rf_asS4 // asS4 unused #define attribute_hidden // attribute_hidden used 170 times in 15 packages #define attribute_visible // attribute_visible used 14 times in lfe, rgl, quadprog, data.table, chebpol, rstan, rmongodb, TPmsm, MonoPoly, bibtex #define bessel_i Rf_bessel_i // bessel_i used 29 times in BiTrinA, Binarize, overlap, RCALI, Hankel, Rcpp11, rotations, Rcpp, moveHMM, dti #define bessel_i_ex Rf_bessel_i_ex // bessel_i_ex used 5 times in Rcpp, Rcpp11, dti #define bessel_j Rf_bessel_j // bessel_j used 25 times in SpatialExtremes, constrainedKriging, BH, Rcpp, RandomFields, Rcpp11 #define bessel_j_ex Rf_bessel_j_ex // bessel_j_ex used 4 times in Rcpp, Rcpp11 #define bessel_k Rf_bessel_k // bessel_k used 127 times in 26 packages #define bessel_k_ex Rf_bessel_k_ex // bessel_k_ex used 9 times in geostatsp, Rcpp, tgp, Rcpp11 #define bessel_y Rf_bessel_y // bessel_y used 4 times in Rcpp, Rcpp11 #define bessel_y_ex Rf_bessel_y_ex // bessel_y_ex used 4 times in Rcpp, Rcpp11 #define beta Rf_beta // beta used 32773 times in 615 packages #define cPsort Rf_cPsort // cPsort unused #define call_S call_R // call_S used 2 times in locfit #define choose Rf_choose // choose used 1368 times in 287 packages #define classgets Rf_classgets // classgets used 91 times in 30 packages #define coerceVector Rf_coerceVector // coerceVector used 2585 times in 167 packages #define col2name Rf_col2name // col2name used 2 times in tikzDevice #define conformable Rf_conformable // conformable used 141 times in 22 packages #define cons Rf_cons // cons used 609 times in 39 packages #define copyListMatrix Rf_copyListMatrix // copyListMatrix used 1 times in Matrix #define copyMatrix Rf_copyMatrix // copyMatrix used 7 times in BDgraph, Matrix, kza #define copyMostAttrib Rf_copyMostAttrib // copyMostAttrib used 68 times in arules, robustbase, data.table, xts, memisc, proxy, zoo, tau #define copyVector Rf_copyVector // copyVector used 12 times in tm, kza, mlegp, adaptivetau #define countContexts Rf_countContexts // countContexts unused #define curDevice Rf_curDevice // curDevice used 4 times in qtutils, showtext, tkrplot #define dbeta Rf_dbeta // dbeta used 377 times in 54 packages #define dbinom Rf_dbinom // dbinom used 290 times in 40 packages #define dbinom_raw Rf_dbinom_raw // dbinom_raw used 50 times in igraph, MCMCpack, secr, AdaptFitOS, phcfM, gof, MasterBayes, locfit #define dcauchy Rf_dcauchy // dcauchy used 25 times in DPpackage, multimark, vcrpart, kernlab, Rcpp11, RInside, Rcpp, aucm, ordinal, littler #define dchisq Rf_dchisq // dchisq used 57 times in 14 packages #define defineVar Rf_defineVar // defineVar used 218 times in 38 packages #define desc2GEDesc Rf_desc2GEDesc // desc2GEDesc used 5 times in Cairo, JavaGD, cairoDevice #define dexp Rf_dexp // dexp used 646 times in 82 packages #define df Rf_df // df unused #define dgamma Rf_dgamma // dgamma used 617 times in 57 packages #define dgeom Rf_dgeom // dgeom used 16 times in RInside, Rcpp, ergm.count, Rcpp11, littler #define dhyper Rf_dhyper // dhyper used 14 times in AdaptFitOS, Rcpp11, RInside, Rcpp, CorrBin, locfit, littler #define digamma Rf_digamma // digamma used 20689 times in 54 packages #define dimgets Rf_dimgets // dimgets used 3 times in CorrBin #define dimnamesgets Rf_dimnamesgets // dimnamesgets used 24 times in Matrix, RxCEcolInf, lxb, sapa #define dlnorm Rf_dlnorm // dlnorm used 68 times in 22 packages #define dlogis Rf_dlogis // dlogis used 91 times in 18 packages #define dnbeta Rf_dnbeta // dnbeta used 6 times in Rcpp, Rcpp11 #define dnbinom Rf_dnbinom // dnbinom used 170 times in 27 packages #define dnbinom_mu Rf_dnbinom_mu // dnbinom_mu used 18 times in RDS, KFAS, Rcpp11, unmarked, Rcpp, sspse, Bclim #define dnchisq Rf_dnchisq // dnchisq used 7 times in spc, Rcpp, Rcpp11 #define dnf Rf_dnf // dnf used 13 times in RxODE, Rcpp, Rcpp11 #define dnorm Rf_dnorm4 // dnorm used 1377 times in 151 packages #define dnorm4 Rf_dnorm4 // dnorm4 used 27 times in 11 packages #define dnt Rf_dnt // dnt used 17 times in alineR, DNAtools, gmum.r, Rcpp11, Rcpp, bayesLife, spc #define doKeybd Rf_doKeybd // doKeybd used 2 times in cairoDevice #define doMouseEvent Rf_doMouseEvent // doMouseEvent used 6 times in cairoDevice #define dpois Rf_dpois // dpois used 212 times in 37 packages #define dpois_raw Rf_dpois_raw // dpois_raw used 25 times in igraph, MCMCpack, AdaptFitOS, phcfM, gof, MasterBayes, locfit #define dpsifn Rf_dpsifn // dpsifn used 4 times in Rcpp, Rcpp11 #define dsignrank Rf_dsignrank // dsignrank used 7 times in RInside, Rcpp, fuzzyRankTests, Rcpp11, littler #define dt Rf_dt // dt unused #define dtukey Rf_dtukey // dtukey used 5 times in timereg, Rcpp, Rcpp11 #define dunif Rf_dunif // dunif used 120 times in 18 packages #define duplicate Rf_duplicate // duplicate used 2088 times in 224 packages #define duplicated Rf_duplicated // duplicated used 402 times in 100 packages #define dweibull Rf_dweibull // dweibull used 38 times in 16 packages #define dwilcox Rf_dwilcox // dwilcox used 12 times in clinfun, fuzzyRankTests, Rcpp11, RInside, Rcpp, DescTools, littler #define elt Rf_elt // elt used 2310 times in 37 packages #define error Rf_error // error used 63771 times in 1109 packages #define error_return(msg) { Rf_error(msg); return R_NilValue; } // error_return used 100 times in rpg, RPostgreSQL, Rook, git2r, grr, rJava, rmumps #define errorcall Rf_errorcall // errorcall used 103 times in RCurl, arules, XML, arulesSequences, pbdMPI, xts, proxy, cba, rJava, RSAP #define errorcall_return(cl,msg) { Rf_errorcall(cl, msg); return R_NilValue; } // errorcall_return used 31 times in Runuran #define eval Rf_eval // eval used 25178 times in 269 packages #define findFun Rf_findFun // findFun used 13 times in sprint, tikzDevice, yaml, unfoldr, TraMineR, RGtk2 #define findVar Rf_findVar // findVar used 1333 times in 24 packages #define findVarInFrame Rf_findVarInFrame // findVarInFrame used 101 times in 13 packages #define findVarInFrame3 Rf_findVarInFrame3 // findVarInFrame3 used 5 times in datamap #define fmax2 Rf_fmax2 // fmax2 used 345 times in 60 packages #define fmin2 Rf_fmin2 // fmin2 used 224 times in 46 packages #define formatComplex Rf_formatComplex // formatComplex unused #define formatInteger Rf_formatInteger // formatInteger used 2 times in qtbase, RGtk2 #define formatLogical Rf_formatLogical // formatLogical used 2 times in qtbase, RGtk2 #define formatReal Rf_formatReal // formatReal used 5 times in data.table, qtbase, RGtk2 #define fprec Rf_fprec // fprec used 38 times in wfe, Rcpp, msm, list, Rcpp11 #define fromDeviceHeight GEfromDeviceHeight // fromDeviceHeight unused #define fromDeviceWidth GEfromDeviceWidth // fromDeviceWidth unused #define fromDeviceX GEfromDeviceX // fromDeviceX used 1 times in RSVGTipsDevice #define fromDeviceY GEfromDeviceY // fromDeviceY unused #define fround Rf_fround // fround used 13 times in bioPN, exactLoglinTest, frontiles, Rcpp11, FRESA.CAD, Rcpp, rmetasim, treethresh #define fsign Rf_fsign // fsign used 66 times in 15 packages #define ftrunc Rf_ftrunc // ftrunc used 123 times in 22 packages #define gammafn Rf_gammafn // gammafn used 374 times in 46 packages #define getAttrib Rf_getAttrib // getAttrib used 1930 times in 239 packages #define getCharCE Rf_getCharCE // getCharCE used 16 times in ore, RSclient, PythonInR, Rserve, jsonlite, tau, rJava #define gsetVar Rf_gsetVar // gsetVar used 4 times in RSVGTipsDevice, Cairo, RSvgDevice, JavaGD #define iPsort Rf_iPsort // iPsort used 3 times in matrixStats, robustbase #define imax2 Rf_imax2 // imax2 used 150 times in 37 packages #define imin2 Rf_imin2 // imin2 used 193 times in 28 packages #define inherits Rf_inherits // inherits used 814 times in 80 packages #define install Rf_install // install used 3178 times in 224 packages #define installChar Rf_installChar // installChar used 4 times in dplyr #define installDDVAL Rf_installDDVAL // installDDVAL unused #define installS3Signature Rf_installS3Signature // installS3Signature unused #define isArray Rf_isArray // isArray used 34 times in checkmate, PythonInR, data.table, ifultools, Rblpapi, Rvcg, unfoldr, TMB, kza, qtbase #define isBasicClass Rf_isBasicClass // isBasicClass unused #define isBlankString Rf_isBlankString // isBlankString used 1 times in iotools #define isByteCode(x) (((x)->sxpinfo.type)==21) // isByteCode unused #define isComplex(s) (((s)->sxpinfo.type) == 15) // isComplex used 119 times in checkmate, PythonInR, ifultools, Rblpapi, Rcpp11, rmatio, stringi, Matrix, qtbase #define isEnvironment(s) (((s)->sxpinfo.type) == 4) // isEnvironment used 113 times in 52 packages #define isExpression(s) (((s)->sxpinfo.type) == 20) // isExpression used 3 times in PythonInR, Rcpp11 #define isFactor Rf_isFactor // isFactor used 42 times in checkmate, rggobi, PythonInR, data.table, Kmisc, partykit, cba, qtbase, RSQLite #define isFrame Rf_isFrame // isFrame used 15 times in checkmate, splusTimeDate, OjaNP, PythonInR, data.table, robfilter #define isFree Rf_isFree // isFree unused #define isFunction Rf_isFunction // isFunction used 274 times in 43 packages #define isInteger Rf_isInteger // isInteger used 402 times in 77 packages #define isLanguage Rf_isLanguage // isLanguage used 63 times in PythonInR, rgp, RandomFields #define isList Rf_isList // isList used 40 times in 11 packages #define isLogical(s) (((s)->sxpinfo.type) == 10) // isLogical used 215 times in 53 packages #define isMatrix Rf_isMatrix // isMatrix used 293 times in 65 packages #define isNewList Rf_isNewList // isNewList used 103 times in 27 packages #define isNull(s) (((s)->sxpinfo.type) == 0) // isNull used 1915 times in 119 packages #define isNumber Rf_isNumber // isNumber used 14 times in PythonInR, readr, stringi, qtbase #define isNumeric Rf_isNumeric // isNumeric used 468 times in 49 packages #define isObject(s) (((s)->sxpinfo.obj) != 0) // isObject used 11 times in dplyr, Rcpp, PythonInR, Rcpp11, stringi, rmumps #define isOrdered Rf_isOrdered // isOrdered used 65 times in partykit, PythonInR, data.table, RSQLite #define isPairList Rf_isPairList // isPairList used 2 times in PythonInR #define isPrimitive Rf_isPrimitive // isPrimitive used 7 times in PythonInR, qtbase #define isReal(s) (((s)->sxpinfo.type) == 14) // isReal used 323 times in 64 packages #define isS4 Rf_isS4 // isS4 used 13 times in PythonInR, Rcpp11, dplyr, Rcpp, catnet, rmumps, sdnet #define isString(s) (((s)->sxpinfo.type) == 16) // isString used 280 times in 59 packages #define isSymbol(s) (((s)->sxpinfo.type) == 1) // isSymbol used 68 times in PythonInR, data.table, Rcpp11, stringi, rgp, dbarts, rJava, sourcetools #define isTs Rf_isTs // isTs used 2 times in PythonInR #define isUnordered Rf_isUnordered // isUnordered used 2 times in PythonInR #define isUnsorted Rf_isUnsorted // isUnsorted unused #define isUserBinop Rf_isUserBinop // isUserBinop used 2 times in PythonInR #define isValidString Rf_isValidString // isValidString used 26 times in SSN, PythonInR, foreign, pbdMPI, RJSONIO, SASxport #define isValidStringF Rf_isValidStringF // isValidStringF used 2 times in PythonInR #define isVector Rf_isVector // isVector used 182 times in 46 packages #define isVectorAtomic Rf_isVectorAtomic // isVectorAtomic used 40 times in bit, matrixStats, checkmate, PythonInR, data.table, Matrix, bit64, potts, aster2, qtbase #define isVectorList Rf_isVectorList // isVectorList used 12 times in RPostgreSQL, spsurvey, PythonInR, stringi, adaptivetau, PCICt, RandomFields #define isVectorizable Rf_isVectorizable // isVectorizable used 3 times in PythonInR, robfilter #define jump_to_toplevel Rf_jump_to_toplevel // jump_to_toplevel used 1 times in rJava #define killDevice Rf_killDevice // killDevice used 3 times in tkrplot #define lang1 Rf_lang1 // lang1 used 30 times in 11 packages #define lang2 Rf_lang2 // lang2 used 216 times in 75 packages #define lang3 Rf_lang3 // lang3 used 107 times in 28 packages #define lang4 Rf_lang4 // lang4 used 65 times in 21 packages #define lang5 Rf_lang5 // lang5 used 11 times in PBSddesolve, GNE, SMC #define lang6 Rf_lang6 // lang6 used 2 times in GNE #define lastElt Rf_lastElt // lastElt unused #define lazy_duplicate Rf_lazy_duplicate // lazy_duplicate unused #define lbeta Rf_lbeta // lbeta used 213 times in 23 packages #define lchoose Rf_lchoose // lchoose used 54 times in 17 packages #define lcons Rf_lcons // lcons used 16 times in rmgarch #define leftButton 1 // leftButton unused #define length(x) Rf_length(x) // length used 44060 times in 1224 packages #define lengthgets Rf_lengthgets // lengthgets used 47 times in 11 packages #define lgamma1p Rf_lgamma1p // lgamma1p used 14 times in Rcpp, OpenMx, ergm.count, heavy, mixAK, Rcpp11 #define lgammafn Rf_lgammafn // lgammafn used 407 times in 66 packages #define lgammafn_sign Rf_lgammafn_sign // lgammafn_sign used 4 times in Rcpp, Rcpp11 #define list1 Rf_list1 // list1 used 197 times in 11 packages #define list2 Rf_list2 // list2 used 441 times in 12 packages #define list3 Rf_list3 // list3 used 72 times in marked, Rdsdp, BH, svd #define list4 Rf_list4 // list4 used 58 times in igraph, PBSddesolve, Rserve, BH, yaml, treethresh, SMC #define list5 Rf_list5 // list5 used 63 times in Rdsdp, BH #define listAppend Rf_listAppend // listAppend used 1 times in ore #define log1pmx Rf_log1pmx // log1pmx used 20 times in DPpackage, BH, Rcpp, Rcpp11 #define logspace_add Rf_logspace_add // logspace_add used 21 times in sna, BMN, Rcpp11, RxCEcolInf, SamplerCompare, STAR, Rcpp #define logspace_sub Rf_logspace_sub // logspace_sub used 16 times in sna, Rcpp11, SamplerCompare, truncnorm, STAR, Rcpp, bfp #define mainloop Rf_mainloop // mainloop unused #define match Rf_match // match used 8773 times in 388 packages #define matchE Rf_matchE // matchE unused #define middleButton 2 // middleButton unused #define mkChar Rf_mkChar // mkChar used 4545 times in 287 packages #define mkCharCE Rf_mkCharCE // mkCharCE used 72 times in 15 packages #define mkCharLen Rf_mkCharLen // mkCharLen used 38 times in 16 packages #define mkCharLenCE Rf_mkCharLenCE // mkCharLenCE used 23 times in 11 packages #define mkNamed Rf_mkNamed // mkNamed used 12 times in RCassandra, coxme, SamplerCompare, survival, JavaGD, DEoptim, qtbase #define mkString Rf_mkString // mkString used 814 times in 96 packages #define namesgets Rf_namesgets // namesgets used 80 times in 14 packages #define ncols Rf_ncols // ncols used 3805 times in 182 packages #define ndevNumber Rf_ndevNumber // ndevNumber used 11 times in Cairo, JavaGD, cairoDevice #define nextDevice Rf_nextDevice // nextDevice used 3 times in rgl #define nlevels Rf_nlevels // nlevels used 546 times in 26 packages #define nrows Rf_nrows // nrows used 4332 times in 215 packages #define nthcdr Rf_nthcdr // nthcdr used 9 times in sprint, rmongodb, PythonInR, xts #define onintr Rf_onintr // onintr used 1 times in rJava #define pbeta Rf_pbeta // pbeta used 262 times in 39 packages #define pbeta_raw Rf_pbeta_raw // pbeta_raw used 10 times in MCMCpack, MasterBayes, Rcpp, phcfM, gof, Rcpp11 #define pbinom Rf_pbinom // pbinom used 53 times in 16 packages #define pcauchy Rf_pcauchy // pcauchy used 25 times in DPpackage, vcrpart, Rcpp11, RInside, Rcpp, ordinal, RandomFields, littler #define pchisq Rf_pchisq // pchisq used 152 times in 33 packages #define pentagamma Rf_pentagamma // pentagamma used 8 times in Rcpp, Rcpp11 #define pexp Rf_pexp // pexp used 117 times in 26 packages #define pf Rf_pf // pf unused #define pgamma Rf_pgamma // pgamma used 164 times in 40 packages #define pgeom Rf_pgeom // pgeom used 10 times in RInside, Rcpp, Rcpp11, littler #define phyper Rf_phyper // phyper used 17 times in Runuran, Rcpp11, cpm, RInside, Rcpp, RandomFields, vegan, littler #define plnorm Rf_plnorm // plnorm used 37 times in 14 packages #define plogis Rf_plogis // plogis used 125 times in 21 packages #define pmatch Rf_pmatch // pmatch used 169 times in ore, git2r, AdaptFitOS, data.table, seqminer, locfit, oce, rmumps #define pnbeta Rf_pnbeta // pnbeta used 23 times in bayesSurv, Rcpp, Rcpp11 #define pnbinom Rf_pnbinom // pnbinom used 29 times in 13 packages #define pnbinom_mu Rf_pnbinom_mu // pnbinom_mu used 3 times in Rcpp, Rcpp11 #define pnchisq Rf_pnchisq // pnchisq used 13 times in spc, Rcpp, Rcpp11 #define pnf Rf_pnf // pnf used 12 times in Rcpp, Rcpp11 #define pnorm Rf_pnorm5 // pnorm used 1582 times in 159 packages #define pnorm5 Rf_pnorm5 // pnorm5 used 77 times in 12 packages #define pnorm_both Rf_pnorm_both // pnorm_both used 12 times in MCMCpack, MasterBayes, Rcpp, phcfM, gof, Rcpp11 #define pnt Rf_pnt // pnt used 111 times in BayesXsrc, hypervolume, Rcpp, spc, Rcpp11 #define ppois Rf_ppois // ppois used 62 times in 18 packages #define prevDevice Rf_prevDevice // prevDevice unused #define printComplexVector Rf_printComplexVector // printComplexVector unused #define printIntegerVector Rf_printIntegerVector // printIntegerVector used 2 times in bvpSolve, deTestSet #define printRealVector Rf_printRealVector // printRealVector used 2 times in bvpSolve, deTestSet #define protect Rf_protect // protect used 599 times in 101 packages #define psigamma Rf_psigamma // psigamma used 9 times in Rcpp, Rcpp11 #define psignrank Rf_psignrank // psignrank used 11 times in FRESA.CAD, RInside, Rcpp, fuzzyRankTests, Rcpp11, littler #define psmatch Rf_psmatch // psmatch used 5 times in rgl #define pt Rf_pt // pt unused #define ptukey Rf_ptukey // ptukey used 6 times in RInside, Rcpp, Rcpp11, littler #define punif Rf_punif // punif used 70 times in 11 packages #define pweibull Rf_pweibull // pweibull used 42 times in 14 packages #define pwilcox Rf_pwilcox // pwilcox used 16 times in fuzzyRankTests, Rcpp11, FRESA.CAD, RInside, simctest, Rcpp, littler #define pythag Rf_pythag // pythag used 105 times in 21 packages #define qbeta Rf_qbeta // qbeta used 57 times in 17 packages #define qbinom Rf_qbinom // qbinom used 18 times in DPpackage, Runuran, BayesXsrc, mvabund, Rcpp11, RInside, Rcpp, ump, littler #define qcauchy Rf_qcauchy // qcauchy used 11 times in RInside, DPpackage, Rcpp, Rcpp11, littler #define qchisq Rf_qchisq // qchisq used 38 times in 21 packages #define qchisq_appr Rf_qchisq_appr // qchisq_appr used 2 times in Rcpp, Rcpp11 #define qexp Rf_qexp // qexp used 20 times in monomvn, GeoGenetix, Rcpp11, icenReg, RInside, TMB, Rcpp, Sunder, RandomFields, littler #define qf Rf_qf // qf unused #define qgamma Rf_qgamma // qgamma used 58 times in 25 packages #define qgeom Rf_qgeom // qgeom used 10 times in RInside, Rcpp, Rcpp11, littler #define qhyper Rf_qhyper // qhyper used 11 times in RInside, Runuran, Rcpp, Rcpp11, littler #define qlnorm Rf_qlnorm // qlnorm used 11 times in icenReg, RInside, Rcpp, Rcpp11, littler #define qlogis Rf_qlogis // qlogis used 16 times in DPpackage, geoBayes, Rcpp11, RInside, TMB, qrjoint, Rcpp, littler #define qnbeta Rf_qnbeta // qnbeta used 8 times in Rcpp, Rcpp11 #define qnbinom Rf_qnbinom // qnbinom used 12 times in RInside, Runuran, Rcpp, mvabund, Rcpp11, littler #define qnbinom_mu Rf_qnbinom_mu // qnbinom_mu used 3 times in Rcpp, Rcpp11 #define qnchisq Rf_qnchisq // qnchisq used 9 times in spc, Rcpp, Rcpp11 #define qnf Rf_qnf // qnf used 8 times in Rcpp, Rcpp11 #define qnorm Rf_qnorm5 // qnorm used 444 times in 96 packages #define qnorm5 Rf_qnorm5 // qnorm5 used 30 times in igraph, PwrGSD, geepack, robustvarComp, Rcpp11, tpr, Rcpp #define qnt Rf_qnt // qnt used 12 times in ore, Rcpp, spc, Rcpp11 #define qpois Rf_qpois // qpois used 23 times in 11 packages #define qsignrank Rf_qsignrank // qsignrank used 6 times in RInside, Rcpp, Rcpp11, littler #define qt Rf_qt // qt unused #define qtukey Rf_qtukey // qtukey used 6 times in RInside, Rcpp, Rcpp11, littler #define qunif Rf_qunif // qunif used 14 times in RInside, qrjoint, Rcpp, Rcpp11, littler #define qweibull Rf_qweibull // qweibull used 16 times in BSquare, Rcpp11, icenReg, RInside, TMB, extWeibQuant, Rcpp, littler #define qwilcox Rf_qwilcox // qwilcox used 10 times in RInside, Rcpp, Rcpp11, littler #define rPsort Rf_rPsort // rPsort used 63 times in 15 packages #define rbeta Rf_rbeta // rbeta used 431 times in 59 packages #define rbinom Rf_rbinom // rbinom used 169 times in 50 packages #define rcauchy Rf_rcauchy // rcauchy used 21 times in PoweR, RInside, Rcpp, DEoptim, Rcpp11, littler #define rchisq Rf_rchisq // rchisq used 244 times in 54 packages #define reEnc Rf_reEnc // reEnc used 3 times in PythonInR, RJSONIO #define readS3VarsFromFrame Rf_readS3VarsFromFrame // readS3VarsFromFrame unused #define revsort Rf_revsort // revsort used 60 times in 20 packages #define rexp Rf_rexp // rexp used 224 times in 56 packages #define rf Rf_rf // rf unused #define rgamma Rf_rgamma // rgamma used 786 times in 104 packages #define rgeom Rf_rgeom // rgeom used 25 times in BSquare, sna, ergm.count, Rcpp11, RInside, Rcpp, littler #define rhyper Rf_rhyper // rhyper used 13 times in kSamples, RInside, Rcpp, Rcpp11, littler #define rightButton 4 // rightButton unused #define rlnorm Rf_rlnorm // rlnorm used 64 times in 18 packages #define rlogis Rf_rlogis // rlogis used 32 times in MCMCpack, phcfM, gof, Rcpp11, MasterBayes, PoweR, RInside, Rcpp, littler #define rmultinom Rf_rmultinom // rmultinom used 42 times in 18 packages #define rnbeta Rf_rnbeta // rnbeta used 4 times in Rcpp, Rcpp11 #define rnbinom Rf_rnbinom // rnbinom used 41 times in 18 packages #define rnbinom_mu Rf_rnbinom_mu // rnbinom_mu used 7 times in Rcpp, Rcpp11 #define rnchisq Rf_rnchisq // rnchisq used 11 times in Rcpp, Rcpp11 #define rnf Rf_rnf // rnf used 35 times in sem, Rcpp, Rcpp11 #define rnorm Rf_rnorm // rnorm used 1865 times in 198 packages #define rnt Rf_rnt // rnt used 2 times in Rcpp, Rcpp11 #define rownamesgets Rf_rownamesgets // rownamesgets unused #define rpois Rf_rpois // rpois used 157 times in 51 packages #define rsignrank Rf_rsignrank // rsignrank used 11 times in RInside, Rcpp, Rcpp11, littler #define rt Rf_rt // rt unused #define rtukey Rf_rtukey // rtukey used 2 times in Rcpp, Rcpp11 #define runif Rf_runif // runif used 2810 times in 273 packages #define rweibull Rf_rweibull // rweibull used 35 times in 12 packages #define rwilcox Rf_rwilcox // rwilcox used 11 times in RInside, Rcpp, Rcpp11, littler #define s_object SEXPREC // s_object used 18563 times in 11 packages #define selectDevice Rf_selectDevice // selectDevice unused #define setAttrib Rf_setAttrib // setAttrib used 1830 times in 251 packages #define setIVector Rf_setIVector // setIVector unused #define setRVector Rf_setRVector // setRVector used 3 times in RcppClassic, RcppClassicExamples #define setSVector Rf_setSVector // setSVector unused #define setVar Rf_setVar // setVar used 24 times in Rhpc, rscproxy, PythonInR, rgenoud, survival, gsl, littler, spatstat #define shallow_duplicate Rf_shallow_duplicate // shallow_duplicate used 2 times in tmlenet, smint #define sign Rf_sign // sign used 5291 times in 389 packages #define str2type Rf_str2type // str2type used 1 times in RGtk2 #define stringPositionTr Rf_stringPositionTr // stringPositionTr unused #define stringSuffix Rf_stringSuffix // stringSuffix unused #define substitute Rf_substitute // substitute used 255 times in 56 packages #define tetragamma Rf_tetragamma // tetragamma used 22 times in Rcpp, Rcpp11, RcppShark #define toDeviceHeight GEtoDeviceHeight // toDeviceHeight unused #define toDeviceWidth GEtoDeviceWidth // toDeviceWidth unused #define toDeviceX GEtoDeviceX // toDeviceX used 1 times in RSVGTipsDevice #define toDeviceY GEtoDeviceY // toDeviceY unused #define topenv Rf_topenv // topenv unused #define translateChar Rf_translateChar // translateChar used 59 times in 19 packages #define translateChar0 Rf_translateChar0 // translateChar0 unused #define translateCharUTF8 Rf_translateCharUTF8 // translateCharUTF8 used 66 times in 13 packages #define trigamma Rf_trigamma // trigamma used 128 times in 24 packages #define type2char Rf_type2char // type2char used 107 times in 12 packages #define type2rstr Rf_type2rstr // type2rstr unused #define type2str Rf_type2str // type2str used 3 times in Kmisc, yaml #define type2str_nowarn Rf_type2str_nowarn // type2str_nowarn used 1 times in qrmtools #define unprotect Rf_unprotect // unprotect used 110 times in 35 packages #define unprotect_ptr Rf_unprotect_ptr // unprotect_ptr unused #define warning Rf_warning // warning used 7679 times in 434 packages #define warningcall Rf_warningcall // warningcall used 4 times in RInside, jsonlite, pbdMPI #define warningcall_immediate Rf_warningcall_immediate // warningcall_immediate used 2 times in Runuran #define xlength(x) Rf_xlength(x) // xlength used 186 times in stringdist, yuima, matrixStats, Rhpc, validate, checkmate, dplR, Rdsdp, pscl, DescTools #define xlengthgets Rf_xlengthgets // xlengthgets unused # /Users/ls/Source/git/fastr/com.oracle.truffle.r.native/include/R.h typedef double Sfloat; // Sfloat used 440 times in AnalyzeFMRI, wavethresh, IGM.MEA, spatial, LS2W, robust, MASS, PBSmapping typedef int Sint; // Sint used 2750 times in 48 packages extern "C" { void R_FlushConsole(void); // R_FlushConsole used 651 times in 78 packages void R_ProcessEvents(void); // R_ProcessEvents used 275 times in 39 packages } # /Users/ls/Source/git/fastr/com.oracle.truffle.r.native/include/R_ext/Applic.h extern "C" { typedef void integr_fn(double *x, int n, void *ex); void Rdqags(integr_fn f, void *ex, double *a, double *b, // Rdqags used 58 times in 18 packages double *epsabs, double *epsrel, double *result, double *abserr, int *neval, int *ier, int *limit, int *lenw, int *last, int *iwork, double *work); void Rdqagi(integr_fn f, void *ex, double *bound, int *inf, // Rdqagi used 33 times in 11 packages double *epsabs, double *epsrel, double *result, double *abserr, int *neval, int *ier, int *limit, int *lenw, int *last, int *iwork, double *work); typedef double optimfn(int, double *, void *); typedef void optimgr(int, double *, double *, void *); void vmmin(int n, double *b, double *Fmin, // vmmin used 62 times in rstpm2, glmmML, RCPmod, SpeciesMix, nnet, fanc, pcaPP, dti, eha, MASS optimfn fn, optimgr gr, int maxit, int trace, int *mask, double abstol, double reltol, int nREPORT, void *ex, int *fncount, int *grcount, int *fail); void nmmin(int n, double *Bvec, double *X, double *Fmin, optimfn fn, // nmmin used 19 times in AnalyzeFMRI, rstpm2, forecast, phyclust, pcaPP, bda, eha, oce int *fail, double abstol, double intol, void *ex, double alpha, double bet, double gamm, int trace, int *fncount, int maxit); void cgmin(int n, double *Bvec, double *X, double *Fmin, // cgmin used 1 times in pcaPP optimfn fn, optimgr gr, int *fail, double abstol, double intol, void *ex, int type, int trace, int *fncount, int *grcount, int maxit); void lbfgsb(int n, int m, double *x, double *l, double *u, int *nbd, // lbfgsb used 34 times in Iboot, PoweR, geostatsp, glmmML, laGP, CorrBin, abn, dti, eha double *Fmin, optimfn fn, optimgr gr, int *fail, void *ex, double factr, double pgtol, int *fncount, int *grcount, int maxit, char *msg, int trace, int nREPORT); void samin(int n, double *pb, double *yb, optimfn fn, int maxit, // samin used 4 times in icenReg, rEDM, RcppEigen, pcaPP int tmax, double ti, int trace, void *ex); int findInterval(double *xt, int n, double x, // findInterval used 11 times in BSquare, DNAprofiles, unfoldr, chebpol, pomp, eco, protViz, PBSmapping, spatstat Rboolean rightmost_closed, Rboolean all_inside, int ilo, int *mflag); void dqrqty_(double *x, int *n, int *k, double *qraux, // dqrqty_ unused double *y, int *ny, double *qty); void dqrqy_(double *x, int *n, int *k, double *qraux, // dqrqy_ unused double *y, int *ny, double *qy); void dqrcf_(double *x, int *n, int *k, double *qraux, // dqrcf_ used 1 times in TwoPhaseInd double *y, int *ny, double *b, int *info); void dqrrsd_(double *x, int *n, int *k, double *qraux, // dqrrsd_ unused double *y, int *ny, double *rsd); void dqrxb_(double *x, int *n, int *k, double *qraux, // dqrxb_ unused double *y, int *ny, double *xb); double R_pretty(double *lo, double *up, int *ndiv, int min_n, // R_pretty used 1 times in rgl double shrink_sml, double high_u_fact[], int eps_correction, int return_bounds); typedef void (*fcn_p)(int, double *, double *, void *); typedef void (*d2fcn_p)(int, int, double *, double *, void *); void fdhess(int n, double *x, double fval, fcn_p fun, void *state, // fdhess used 16 times in sem, fArma, fracdiff double *h, int nfd, double *step, double *f, int ndigit, double *typx); void optif9(int nr, int n, double *x, // optif9 used 17 times in sem, rstpm2, nlme, pcaPP fcn_p fcn, fcn_p d1fcn, d2fcn_p d2fcn, void *state, double *typsiz, double fscale, int method, int iexp, int *msg, int ndigit, int itnlim, int iagflg, int iahflg, double dlt, double gradtl, double stepmx, double steptl, double *xpls, double *fpls, double *gpls, int *itrmcd, double *a, double *wrk, int *itncnt); void dqrdc2_(double *x, int *ldx, int *n, int *p, // dqrdc2_ used 4 times in earth, TwoPhaseInd double *tol, int *rank, double *qraux, int *pivot, double *work); void dqrls_(double *x, int *n, int *p, double *y, int *ny, // dqrls_ used 8 times in DatABEL, GenABEL, VariABEL double *tol, double *b, double *rsd, double *qty, int *k, int *jpvt, double *qraux, double *work); } # /Users/ls/Source/git/fastr/com.oracle.truffle.r.native/include/R_ext/Arith.h extern "C" { extern double R_NaN; // R_NaN used 469 times in 68 packages extern double R_PosInf; // R_PosInf used 562 times in 112 packages extern double R_NegInf; // R_NegInf used 699 times in 105 packages extern double R_NaReal; // R_NaReal used 140 times in 34 packages // NA_REAL used 1667 times in 226 packages extern int R_NaInt; // R_NaInt used 58 times in 20 packages // NA_LOGICAL used 355 times in 73 packages // NA_INTEGER used 1520 times in 183 packages int R_IsNA(double); // R_IsNA used 161 times in 40 packages int R_IsNaN(double); // R_IsNaN used 75 times in 28 packages int R_finite(double); // R_finite used 232 times in 44 packages int R_isnancpp(double); // R_isnancpp used 8 times in igraph, PwrGSD } # /Users/ls/Source/git/fastr/com.oracle.truffle.r.native/include/R_ext/BLAS.h extern "C" { extern double dasum_(const int *n, const double *dx, const int *incx); extern void daxpy_(const int *n, const double *alpha, const double *dx, const int *incx, double *dy, const int *incy); extern void dcopy_(const int *n, const double *dx, const int *incx, double *dy, const int *incy); extern double ddot_(const int *n, const double *dx, const int *incx, const double *dy, const int *incy); extern double dnrm2_(const int *n, const double *dx, const int *incx); extern void drot_(const int *n, double *dx, const int *incx, double *dy, const int *incy, const double *c, const double *s); extern void drotg_(const double *a, const double *b, double *c, double *s); extern void drotm_(const int *n, double *dx, const int *incx, double *dy, const int *incy, const double *dparam); extern void drotmg_(const double *dd1, const double *dd2, const double *dx1, const double *dy1, double *param); extern void dscal_(const int *n, const double *alpha, double *dx, const int *incx); extern void dswap_(const int *n, double *dx, const int *incx, double *dy, const int *incy); extern int idamax_(const int *n, const double *dx, const int *incx); extern void dgbmv_(const char *trans, const int *m, const int *n, const int *kl,const int *ku, const double *alpha, const double *a, const int *lda, const double *x, const int *incx, const double *Rf_beta, double *y, const int *incy); extern void dgemv_(const char *trans, const int *m, const int *n, const double *alpha, const double *a, const int *lda, const double *x, const int *incx, const double *Rf_beta, double *y, const int *incy); extern void dsbmv_(const char *uplo, const int *n, const int *k, const double *alpha, const double *a, const int *lda, const double *x, const int *incx, const double *Rf_beta, double *y, const int *incy); extern void dspmv_(const char *uplo, const int *n, const double *alpha, const double *ap, const double *x, const int *incx, const double *Rf_beta, double *y, const int *incy); extern void dsymv_(const char *uplo, const int *n, const double *alpha, const double *a, const int *lda, const double *x, const int *incx, const double *Rf_beta, double *y, const int *incy); extern void dtbmv_(const char *uplo, const char *trans, const char *diag, const int *n, const int *k, const double *a, const int *lda, double *x, const int *incx); extern void dtpmv_(const char *uplo, const char *trans, const char *diag, const int *n, const double *ap, double *x, const int *incx); extern void dtrmv_(const char *uplo, const char *trans, const char *diag, const int *n, const double *a, const int *lda, double *x, const int *incx); extern void dtbsv_(const char *uplo, const char *trans, const char *diag, const int *n, const int *k, const double *a, const int *lda, double *x, const int *incx); extern void dtpsv_(const char *uplo, const char *trans, const char *diag, const int *n, const double *ap, double *x, const int *incx); extern void dtrsv_(const char *uplo, const char *trans, const char *diag, const int *n, const double *a, const int *lda, double *x, const int *incx); extern void dger_(const int *m, const int *n, const double *alpha, const double *x, const int *incx, const double *y, const int *incy, double *a, const int *lda); extern void dsyr_(const char *uplo, const int *n, const double *alpha, const double *x, const int *incx, double *a, const int *lda); extern void dspr_(const char *uplo, const int *n, const double *alpha, const double *x, const int *incx, double *ap); extern void dsyr2_(const char *uplo, const int *n, const double *alpha, const double *x, const int *incx, const double *y, const int *incy, double *a, const int *lda); extern void dspr2_(const char *uplo, const int *n, const double *alpha, const double *x, const int *incx, const double *y, const int *incy, double *ap); extern void dgemm_(const char *transa, const char *transb, const int *m, const int *n, const int *k, const double *alpha, const double *a, const int *lda, const double *b, const int *ldb, const double *Rf_beta, double *c, const int *ldc); extern void dtrsm_(const char *side, const char *uplo, const char *transa, const char *diag, const int *m, const int *n, const double *alpha, const double *a, const int *lda, double *b, const int *ldb); extern void dtrmm_(const char *side, const char *uplo, const char *transa, const char *diag, const int *m, const int *n, const double *alpha, const double *a, const int *lda, double *b, const int *ldb); extern void dsymm_(const char *side, const char *uplo, const int *m, const int *n, const double *alpha, const double *a, const int *lda, const double *b, const int *ldb, const double *Rf_beta, double *c, const int *ldc); extern void dsyrk_(const char *uplo, const char *trans, const int *n, const int *k, const double *alpha, const double *a, const int *lda, const double *Rf_beta, double *c, const int *ldc); extern void dsyr2k_(const char *uplo, const char *trans, const int *n, const int *k, const double *alpha, const double *a, const int *lda, const double *b, const int *ldb, const double *Rf_beta, double *c, const int *ldc); extern double dcabs1_(double *z); extern double dzasum_(int *n, Rcomplex *zx, int *incx); extern double dznrm2_(int *n, Rcomplex *x, int *incx); extern int izamax_(int *n, Rcomplex *zx, int *incx); extern void zaxpy_(int *n, Rcomplex *za, Rcomplex *zx, int *incx, Rcomplex *zy, int *incy); extern void zcopy_(int *n, Rcomplex *zx, int *incx, Rcomplex *zy, int *incy); extern Rcomplex zdotc_(int *n, Rcomplex *zx, int *incx, Rcomplex *zy, int *incy); extern Rcomplex zdotu_(int *n, Rcomplex *zx, int *incx, Rcomplex *zy, int *incy); extern void zdrot_(int *n, Rcomplex *zx, int *incx, Rcomplex *zy, int *incy, double *c, double *s); extern void zdscal_(int *n, double *da, Rcomplex *zx, int *incx); extern void zgbmv_(char *trans, int *m, int *n, int *kl, int *ku, Rcomplex *alpha, Rcomplex *a, int *lda, Rcomplex *x, int *incx, Rcomplex *Rf_beta, Rcomplex *y, int *incy); extern void zgemm_(const char *transa, const char *transb, const int *m, const int *n, const int *k, const Rcomplex *alpha, const Rcomplex *a, const int *lda, const Rcomplex *b, const int *ldb, const Rcomplex *Rf_beta, Rcomplex *c, const int *ldc); extern void zgemv_(char *trans, int *m, int *n, Rcomplex *alpha, Rcomplex *a, int *lda, Rcomplex *x, int *incx, Rcomplex *Rf_beta, Rcomplex *y, int * incy); extern void zgerc_(int *m, int *n, Rcomplex *alpha, Rcomplex *x, int *incx, Rcomplex *y, int *incy, Rcomplex *a, int *lda); extern void zgeru_(int *m, int *n, Rcomplex *alpha, Rcomplex *x, int *incx, Rcomplex *y, int *incy, Rcomplex *a, int *lda); extern void zhbmv_(char *uplo, int *n, int *k, Rcomplex *alpha, Rcomplex *a, int *lda, Rcomplex *x, int *incx, Rcomplex *Rf_beta, Rcomplex *y, int *incy); extern void zhemm_(char *side, char *uplo, int *m, int *n, Rcomplex *alpha, Rcomplex *a, int *lda, Rcomplex *b, int *ldb, Rcomplex *Rf_beta, Rcomplex *c, int *ldc); extern void zhemv_(char *uplo, int *n, Rcomplex *alpha, Rcomplex *a, int *lda, Rcomplex *x, int *incx, Rcomplex *Rf_beta, Rcomplex *y, int *incy); extern void zher_(char *uplo, int *n, double *alpha, Rcomplex *x, int *incx, Rcomplex *a, int *lda); extern void zher2_(char *uplo, int *n, Rcomplex *alpha, Rcomplex *x, int *incx, Rcomplex *y, int *incy, Rcomplex *a, int *lda); extern void zher2k_(char *uplo, char *trans, int *n, int *k, Rcomplex *alpha, Rcomplex *a, int *lda, Rcomplex *b, int *ldb, double *Rf_beta, Rcomplex *c, int *ldc); extern void zherk_(char *uplo, char *trans, int *n, int *k, double *alpha, Rcomplex *a, int *lda, double *Rf_beta, Rcomplex *c, int *ldc); extern void zhpmv_(char *uplo, int *n, Rcomplex *alpha, Rcomplex *ap, Rcomplex *x, int *incx, Rcomplex * Rf_beta, Rcomplex *y, int *incy); extern void zhpr_(char *uplo, int *n, double *alpha, Rcomplex *x, int *incx, Rcomplex *ap); extern void zhpr2_(char *uplo, int *n, Rcomplex *alpha, Rcomplex *x, int *incx, Rcomplex *y, int *incy, Rcomplex *ap); extern void zrotg_(Rcomplex *ca, Rcomplex *cb, double *c, Rcomplex *s); extern void zscal_(int *n, Rcomplex *za, Rcomplex *zx, int *incx); extern void zswap_(int *n, Rcomplex *zx, int *incx, Rcomplex *zy, int *incy); extern void zsymm_(char *side, char *uplo, int *m, int *n, Rcomplex *alpha, Rcomplex *a, int *lda, Rcomplex *b, int *ldb, Rcomplex *Rf_beta, Rcomplex *c, int *ldc); extern void zsyr2k_(char *uplo, char *trans, int *n, int *k, Rcomplex *alpha, Rcomplex *a, int *lda, Rcomplex *b, int *ldb, Rcomplex *Rf_beta, Rcomplex *c, int *ldc); extern void zsyrk_(char *uplo, char *trans, int *n, int *k, Rcomplex *alpha, Rcomplex *a, int *lda, Rcomplex *Rf_beta, Rcomplex *c, int *ldc); extern void ztbmv_(char *uplo, char *trans, char *diag, int *n, int *k, Rcomplex *a, int *lda, Rcomplex *x, int *incx); extern void ztbsv_(char *uplo, char *trans, char *diag, int *n, int *k, Rcomplex *a, int *lda, Rcomplex *x, int *incx); extern void ztpmv_(char *uplo, char *trans, char *diag, int *n, Rcomplex *ap, Rcomplex *x, int *incx); extern void ztpsv_(char *uplo, char *trans, char *diag, int *n, Rcomplex *ap, Rcomplex *x, int *incx); extern void ztrmm_(char *side, char *uplo, char *transa, char *diag, int *m, int *n, Rcomplex *alpha, Rcomplex *a, int *lda, Rcomplex *b, int *ldb); extern void ztrmv_(char *uplo, char *trans, char *diag, int *n, Rcomplex *a, int *lda, Rcomplex *x, int *incx); extern void ztrsm_(char *side, char *uplo, char *transa, char *diag, int *m, int *n, Rcomplex *alpha, Rcomplex *a, int *lda, Rcomplex *b, int *ldb); extern void ztrsv_(char *uplo, char *trans, char *diag, int *n, Rcomplex *a, int *lda, Rcomplex *x, int *incx); } # /Users/ls/Source/git/fastr/com.oracle.truffle.r.native/include/R_ext/Boolean.h extern "C" { typedef enum { FALSE = 0, TRUE } Rboolean; } # /Users/ls/Source/git/fastr/com.oracle.truffle.r.native/include/R_ext/Callbacks.h typedef Rboolean (*R_ToplevelCallback)(SEXP expr, SEXP value, Rboolean succeeded, Rboolean visible, void *); typedef struct _ToplevelCallback R_ToplevelCallbackEl; struct _ToplevelCallback { R_ToplevelCallback cb; void *data; void (*finalizer)(void *data); char *name; R_ToplevelCallbackEl *next; }; extern "C" { Rboolean Rf_removeTaskCallbackByIndex(int id); // Rf_removeTaskCallbackByIndex unused Rboolean Rf_removeTaskCallbackByName(const char *name); // Rf_removeTaskCallbackByName unused SEXP R_removeTaskCallback(SEXP which); // R_removeTaskCallback unused R_ToplevelCallbackEl* Rf_addTaskCallback(R_ToplevelCallback cb, void *data, void (*finalizer)(void *), const char *name, int *pos); typedef struct _R_ObjectTable R_ObjectTable; typedef Rboolean (*Rdb_exists)(const char * const name, Rboolean *canCache, R_ObjectTable *); typedef SEXP (*Rdb_get)(const char * const name, Rboolean *canCache, R_ObjectTable *); typedef int (*Rdb_remove)(const char * const name, R_ObjectTable *); typedef SEXP (*Rdb_assign)(const char * const name, SEXP value, R_ObjectTable *); typedef SEXP (*Rdb_objects)(R_ObjectTable *); typedef Rboolean (*Rdb_canCache)(const char * const name, R_ObjectTable *); typedef void (*Rdb_onDetach)(R_ObjectTable *); typedef void (*Rdb_onAttach)(R_ObjectTable *); struct _R_ObjectTable{ int type; char **cachedNames; Rboolean active; Rdb_exists exists; Rdb_get get; Rdb_remove remove; Rdb_assign assign; Rdb_objects objects; Rdb_canCache canCache; Rdb_onDetach onDetach; Rdb_onAttach onAttach; void *privateData; }; } # /Users/ls/Source/git/fastr/com.oracle.truffle.r.native/include/R_ext/Complex.h extern "C" { typedef struct { double r; double i; } Rcomplex; // Rcomplex used 893 times in 47 packages } # /Users/ls/Source/git/fastr/com.oracle.truffle.r.native/include/R_ext/Connections.h typedef struct Rconn *Rconnection; struct Rconn { char* class; char* description; int enc; char mode[5]; Rboolean text, isopen, incomplete, canread, canwrite, canseek, blocking, isGzcon; Rboolean (*open)(struct Rconn *); void (*close)(struct Rconn *); void (*destroy)(struct Rconn *); int (*vfprintf)(struct Rconn *, const char *, va_list); int (*fgetc)(struct Rconn *); int (*fgetc_internal)(struct Rconn *); double (*seek)(struct Rconn *, double, int, int); void (*truncate)(struct Rconn *); int (*fflush)(struct Rconn *); size_t (*read)(void *, size_t, size_t, struct Rconn *); size_t (*write)(const void *, size_t, size_t, struct Rconn *); int nPushBack, posPushBack; char **PushBack; int save, save2; char encname[101]; void *inconv, *outconv; char iconvbuff[25], oconvbuff[50], *next, init_out[25]; short navail, inavail; Rboolean EOF_signalled; Rboolean UTF8out; void *id; void *ex_ptr; void *private; int status; }; extern "C" { SEXP R_new_custom_connection(const char *description, const char *mode, const char *class_name, Rconnection *ptr); // R_new_custom_connection used 2 times in curl, rredis size_t R_ReadConnection(Rconnection con, void *buf, size_t n); // R_ReadConnection used 1 times in iotools size_t R_WriteConnection(Rconnection con, void *buf, size_t n); // R_WriteConnection used 4 times in Cairo } # /Users/ls/Source/git/fastr/com.oracle.truffle.r.native/include/R_ext/Error.h extern "C" { void __attribute__((noreturn)) Rf_error(const char *, ...); void __attribute__((noreturn)) UNIMPLEMENTED(const char *); void __attribute__((noreturn)) WrongArgCount(const char *); void Rf_warning(const char *, ...); // Rf_warning used 316 times in 66 packages // warning used 7679 times in 434 packages void R_ShowMessage(const char *s); // R_ShowMessage used 104 times in Rserve, rJava, HiPLARM } # /Users/ls/Source/git/fastr/com.oracle.truffle.r.native/include/R_ext/GetX11Image.h extern "C" { Rboolean R_GetX11Image(int d, void *pximage, int *pwidth, int *pheight); // R_GetX11Image used 1 times in tkrplot } # /Users/ls/Source/git/fastr/com.oracle.truffle.r.native/include/R_ext/GraphicsDevice.h extern "C" { typedef struct _DevDesc DevDesc; typedef DevDesc* pDevDesc; struct _DevDesc { double left; double right; double bottom; double top; double clipLeft; double clipRight; double clipBottom; double clipTop; double xCharOffset; double yCharOffset; double yLineBias; double ipr[2]; double cra[2]; double gamma; Rboolean canClip; Rboolean canChangeGamma; int canHAdj; double startps; int startcol; int startfill; int startlty; int startfont; double startgamma; void *deviceSpecific; Rboolean displayListOn; Rboolean canGenMouseDown; Rboolean canGenMouseMove; Rboolean canGenMouseUp; Rboolean canGenKeybd; Rboolean gettingEvent; void (*activate)(const pDevDesc ); void (*circle)(double x, double y, double r, const pGEcontext gc, pDevDesc dd); void (*clip)(double x0, double x1, double y0, double y1, pDevDesc dd); void (*close)(pDevDesc dd); void (*deactivate)(pDevDesc ); Rboolean (*locator)(double *x, double *y, pDevDesc dd); void (*line)(double x1, double y1, double x2, double y2, const pGEcontext gc, pDevDesc dd); void (*metricInfo)(int c, const pGEcontext gc, double* ascent, double* descent, double* width, pDevDesc dd); void (*mode)(int mode, pDevDesc dd); void (*newPage)(const pGEcontext gc, pDevDesc dd); void (*polygon)(int n, double *x, double *y, const pGEcontext gc, pDevDesc dd); void (*polyline)(int n, double *x, double *y, const pGEcontext gc, pDevDesc dd); void (*rect)(double x0, double y0, double x1, double y1, const pGEcontext gc, pDevDesc dd); void (*path)(double *x, double *y, int npoly, int *nper, Rboolean winding, const pGEcontext gc, pDevDesc dd); void (*raster)(unsigned int *raster, int w, int h, double x, double y, double width, double height, double rot, Rboolean interpolate, const pGEcontext gc, pDevDesc dd); SEXP (*cap)(pDevDesc dd); void (*size)(double *left, double *right, double *bottom, double *top, pDevDesc dd); double (*strWidth)(const char *str, const pGEcontext gc, pDevDesc dd); void (*text)(double x, double y, const char *str, double rot, double hadj, const pGEcontext gc, pDevDesc dd); void (*onExit)(pDevDesc dd); SEXP (*getEvent)(SEXP, const char *); Rboolean (*newFrameConfirm)(pDevDesc dd); Rboolean hasTextUTF8; void (*textUTF8)(double x, double y, const char *str, double rot, double hadj, const pGEcontext gc, pDevDesc dd); double (*strWidthUTF8)(const char *str, const pGEcontext gc, pDevDesc dd); Rboolean wantSymbolUTF8; Rboolean useRotatedTextInContour; SEXP eventEnv; // eventEnv used 3 times in cairoDevice, R2SWF void (*eventHelper)(pDevDesc dd, int code); int (*holdflush)(pDevDesc dd, int level); int haveTransparency; int haveTransparentBg; int haveRaster; int haveCapture, haveLocator; char reserved[64]; }; int Rf_ndevNumber(pDevDesc ); // Rf_ndevNumber unused // ndevNumber used 11 times in Cairo, JavaGD, cairoDevice int Rf_NumDevices(void); // Rf_NumDevices unused // NumDevices used 3 times in JavaGD void R_CheckDeviceAvailable(void); // R_CheckDeviceAvailable used 14 times in 12 packages Rboolean R_CheckDeviceAvailableBool(void); // R_CheckDeviceAvailableBool unused int Rf_curDevice(void); // Rf_curDevice unused // curDevice used 4 times in qtutils, showtext, tkrplot int Rf_nextDevice(int); // Rf_nextDevice unused // nextDevice used 3 times in rgl int Rf_prevDevice(int); // Rf_prevDevice unused // prevDevice unused int Rf_selectDevice(int); // Rf_selectDevice unused // selectDevice unused void Rf_killDevice(int); // Rf_killDevice unused // killDevice used 3 times in tkrplot int Rf_NoDevices(void); // Rf_NoDevices unused // NoDevices used 1 times in tkrplot void Rf_NewFrameConfirm(pDevDesc); // Rf_NewFrameConfirm unused // NewFrameConfirm unused typedef enum {knUNKNOWN = -1, knLEFT = 0, knUP, knRIGHT, knDOWN, knF1, knF2, knF3, knF4, knF5, knF6, knF7, knF8, knF9, knF10, knF11, knF12, knPGUP, knPGDN, knEND, knHOME, knINS, knDEL} R_KeyName; typedef enum {meMouseDown = 0, meMouseUp, meMouseMove} R_MouseEvent; void Rf_doMouseEvent(pDevDesc dd, R_MouseEvent event, // Rf_doMouseEvent unused // doMouseEvent used 6 times in cairoDevice int buttons, double x, double y); void Rf_doKeybd(pDevDesc dd, R_KeyName rkey, // Rf_doKeybd unused // doKeybd used 2 times in cairoDevice const char *keyname); extern Rboolean R_interrupts_suspended; // R_interrupts_suspended unused extern int R_interrupts_pending; // R_interrupts_pending used 6 times in igraph, rJava extern void Rf_onintr(void); // Rf_onintr used 216 times in 12 packages // onintr used 1 times in rJava extern Rboolean mbcslocale; // mbcslocale used 7 times in qtutils, RCurl, cairoDevice, Cairo, RSvgDevice, PCICt extern void *Rf_AdobeSymbol2utf8(char*out, const char *in, size_t nwork); // Rf_AdobeSymbol2utf8 unused // AdobeSymbol2utf8 used 2 times in Cairo extern size_t Rf_ucstoutf8(char *s, const unsigned int c); // Rf_ucstoutf8 used 7 times in cairoDevice, Cairo, rvg, svglite } # /Users/ls/Source/git/fastr/com.oracle.truffle.r.native/include/R_ext/GraphicsEngine.h extern "C" { int R_GE_getVersion(void); // R_GE_getVersion unused void R_GE_checkVersionOrDie(int version); // R_GE_checkVersionOrDie used 11 times in qtutils, rscproxy, cairoDevice, RSvgDevice, R2SWF, rvg, RSVGTipsDevice, tikzDevice, svglite typedef enum { GE_DEVICE = 0, GE_NDC = 1, GE_INCHES = 2, GE_CM = 3 } GEUnit; // GEUnit unused typedef enum { GE_InitState = 0, GE_FinaliseState = 1, GE_SaveState = 2, GE_RestoreState = 6, GE_CopyState = 3, GE_SaveSnapshotState = 4, GE_RestoreSnapshotState = 5, GE_CheckPlot = 7, GE_ScalePS = 8 } GEevent; // GEevent unused typedef enum { GE_ROUND_CAP = 1, GE_BUTT_CAP = 2, GE_SQUARE_CAP = 3 } R_GE_lineend; // R_GE_lineend used 3 times in qtutils, Cairo typedef enum { GE_ROUND_JOIN = 1, GE_MITRE_JOIN = 2, GE_BEVEL_JOIN = 3 } R_GE_linejoin; // R_GE_linejoin used 3 times in qtutils, Cairo typedef struct { int col; int fill; double gamma; double lwd; int lty; R_GE_lineend lend; R_GE_linejoin ljoin; double lmitre; double cex; double ps; double lineheight; int fontface; char fontfamily[201]; } R_GE_gcontext; // R_GE_gcontext used 87 times in qtutils, Cairo, RSvgDevice, rvg, RSVGTipsDevice, JavaGD, showtext typedef R_GE_gcontext* pGEcontext; typedef struct _GEDevDesc GEDevDesc; typedef SEXP (* GEcallback)(GEevent, GEDevDesc *, SEXP); typedef struct { void *systemSpecific; GEcallback callback; } GESystemDesc; // GESystemDesc unused struct _GEDevDesc { pDevDesc dev; Rboolean displayListOn; SEXP displayList; // displayList used 30 times in qtutils, rgl, Cairo, JavaGD, R2SWF SEXP DLlastElt; // DLlastElt unused SEXP savedSnapshot; // savedSnapshot used 4 times in qtutils, Cairo, JavaGD Rboolean dirty; Rboolean recordGraphics; GESystemDesc *gesd[256]; Rboolean ask; }; typedef GEDevDesc* pGEDevDesc; pGEDevDesc Rf_desc2GEDesc(pDevDesc dd); // Rf_desc2GEDesc unused // desc2GEDesc used 5 times in Cairo, JavaGD, cairoDevice int GEdeviceNumber(pGEDevDesc); // GEdeviceNumber used 4 times in Cairo, JavaGD pGEDevDesc GEgetDevice(int); // GEgetDevice used 20 times in tikzDevice, Cairo, JavaGD, rvg, showtext void GEaddDevice(pGEDevDesc); // GEaddDevice used 4 times in Cairo, JavaGD void GEaddDevice2(pGEDevDesc, const char *); // GEaddDevice2 used 12 times in qtutils, devEMF, rscproxy, cairoDevice, RSvgDevice, R2SWF, rvg, RSVGTipsDevice, tikzDevice, svglite void GEaddDevice2f(pGEDevDesc, const char *, const char *); // GEaddDevice2f unused void GEkillDevice(pGEDevDesc); // GEkillDevice used 4 times in Cairo, JavaGD, cairoDevice pGEDevDesc GEcreateDevDesc(pDevDesc dev); // GEcreateDevDesc used 14 times in 12 packages void GEdestroyDevDesc(pGEDevDesc dd); // GEdestroyDevDesc unused void *GEsystemState(pGEDevDesc dd, int index); // GEsystemState unused void GEregisterWithDevice(pGEDevDesc dd); // GEregisterWithDevice unused void GEregisterSystem(GEcallback callback, int *systemRegisterIndex); // GEregisterSystem unused void GEunregisterSystem(int registerIndex); // GEunregisterSystem unused SEXP GEhandleEvent(GEevent event, pDevDesc dev, SEXP data); // GEhandleEvent unused double GEfromDeviceX(double value, GEUnit to, pGEDevDesc dd); // GEfromDeviceX unused // fromDeviceX used 1 times in RSVGTipsDevice double GEtoDeviceX(double value, GEUnit from, pGEDevDesc dd); // GEtoDeviceX unused // toDeviceX used 1 times in RSVGTipsDevice double GEfromDeviceY(double value, GEUnit to, pGEDevDesc dd); // GEfromDeviceY unused // fromDeviceY unused double GEtoDeviceY(double value, GEUnit from, pGEDevDesc dd); // GEtoDeviceY unused // toDeviceY unused double GEfromDeviceWidth(double value, GEUnit to, pGEDevDesc dd); // GEfromDeviceWidth unused // fromDeviceWidth unused double GEtoDeviceWidth(double value, GEUnit from, pGEDevDesc dd); // GEtoDeviceWidth unused // toDeviceWidth unused double GEfromDeviceHeight(double value, GEUnit to, pGEDevDesc dd); // GEfromDeviceHeight unused // fromDeviceHeight unused double GEtoDeviceHeight(double value, GEUnit from, pGEDevDesc dd); // GEtoDeviceHeight unused // toDeviceHeight unused typedef unsigned int rcolor; rcolor Rf_RGBpar(SEXP, int); // Rf_RGBpar unused // RGBpar used 3 times in Cairo, jpeg rcolor Rf_RGBpar3(SEXP, int, rcolor); // Rf_RGBpar3 unused // RGBpar3 unused const char *Rf_col2name(rcolor col); // Rf_col2name unused // col2name used 2 times in tikzDevice rcolor R_GE_str2col(const char *s); // R_GE_str2col used 13 times in devEMF, RSVGTipsDevice, tikzDevice, RSvgDevice, rvg, svglite R_GE_lineend GE_LENDpar(SEXP value, int ind); // GE_LENDpar unused SEXP GE_LENDget(R_GE_lineend lend); // GE_LENDget unused R_GE_linejoin GE_LJOINpar(SEXP value, int ind); // GE_LJOINpar unused SEXP GE_LJOINget(R_GE_linejoin ljoin); // GE_LJOINget unused void GESetClip(double x1, double y1, double x2, double y2, pGEDevDesc dd); // GESetClip unused void GENewPage(const pGEcontext gc, pGEDevDesc dd); // GENewPage unused void GELine(double x1, double y1, double x2, double y2, // GELine unused const pGEcontext gc, pGEDevDesc dd); void GEPolyline(int n, double *x, double *y, // GEPolyline unused const pGEcontext gc, pGEDevDesc dd); void GEPolygon(int n, double *x, double *y, // GEPolygon unused const pGEcontext gc, pGEDevDesc dd); SEXP GEXspline(int n, double *x, double *y, double *s, Rboolean open, // GEXspline unused Rboolean repEnds, Rboolean draw, const pGEcontext gc, pGEDevDesc dd); void GECircle(double x, double y, double radius, // GECircle unused const pGEcontext gc, pGEDevDesc dd); void GERect(double x0, double y0, double x1, double y1, // GERect unused const pGEcontext gc, pGEDevDesc dd); void GEPath(double *x, double *y, // GEPath unused int npoly, int *nper, Rboolean winding, const pGEcontext gc, pGEDevDesc dd); void GERaster(unsigned int *raster, int w, int h, // GERaster unused double x, double y, double width, double height, double angle, Rboolean interpolate, const pGEcontext gc, pGEDevDesc dd); SEXP GECap(pGEDevDesc dd); // GECap unused void GEText(double x, double y, const char * const str, cetype_t enc, // GEText unused double xc, double yc, double rot, const pGEcontext gc, pGEDevDesc dd); void GEMode(int mode, pGEDevDesc dd); // GEMode unused void GESymbol(double x, double y, int pch, double size, // GESymbol unused const pGEcontext gc, pGEDevDesc dd); void GEPretty(double *lo, double *up, int *ndiv); // GEPretty unused void GEMetricInfo(int c, const pGEcontext gc, // GEMetricInfo unused double *ascent, double *descent, double *width, pGEDevDesc dd); double GEStrWidth(const char *str, cetype_t enc, // GEStrWidth unused const pGEcontext gc, pGEDevDesc dd); double GEStrHeight(const char *str, cetype_t enc, // GEStrHeight unused const pGEcontext gc, pGEDevDesc dd); void GEStrMetric(const char *str, cetype_t enc, const pGEcontext gc, // GEStrMetric unused double *ascent, double *descent, double *width, pGEDevDesc dd); int GEstring_to_pch(SEXP pch); // GEstring_to_pch unused unsigned int GE_LTYpar(SEXP, int); SEXP GE_LTYget(unsigned int); // GE_LTYget unused void R_GE_rasterScale(unsigned int *sraster, int sw, int sh, // R_GE_rasterScale unused unsigned int *draster, int dw, int dh); void R_GE_rasterInterpolate(unsigned int *sraster, int sw, int sh, // R_GE_rasterInterpolate unused unsigned int *draster, int dw, int dh); void R_GE_rasterRotatedSize(int w, int h, double angle, // R_GE_rasterRotatedSize unused int *wnew, int *hnew); void R_GE_rasterRotatedOffset(int w, int h, double angle, int botleft, // R_GE_rasterRotatedOffset unused double *xoff, double *yoff); void R_GE_rasterResizeForRotation(unsigned int *sraster, // R_GE_rasterResizeForRotation unused int w, int h, unsigned int *newRaster, int wnew, int hnew, const pGEcontext gc); void R_GE_rasterRotate(unsigned int *sraster, int w, int h, double angle, // R_GE_rasterRotate unused unsigned int *draster, const pGEcontext gc, Rboolean perPixelAlpha); double GEExpressionWidth(SEXP expr, // GEExpressionWidth unused const pGEcontext gc, pGEDevDesc dd); double GEExpressionHeight(SEXP expr, // GEExpressionHeight unused const pGEcontext gc, pGEDevDesc dd); void GEExpressionMetric(SEXP expr, const pGEcontext gc, // GEExpressionMetric unused double *ascent, double *descent, double *width, pGEDevDesc dd); void GEMathText(double x, double y, SEXP expr, // GEMathText unused double xc, double yc, double rot, const pGEcontext gc, pGEDevDesc dd); SEXP GEcontourLines(double *x, int nx, double *y, int ny, // GEcontourLines unused double *z, double *levels, int nl); double R_GE_VStrWidth(const char *s, cetype_t enc, const pGEcontext gc, pGEDevDesc dd); // R_GE_VStrWidth unused double R_GE_VStrHeight(const char *s, cetype_t enc, const pGEcontext gc, pGEDevDesc dd); // R_GE_VStrHeight unused void R_GE_VText(double x, double y, const char * const s, cetype_t enc, // R_GE_VText unused double x_justify, double y_justify, double rotation, const pGEcontext gc, pGEDevDesc dd); pGEDevDesc GEcurrentDevice(void); // GEcurrentDevice used 9 times in RSVGTipsDevice, tikzDevice, cairoDevice Rboolean GEdeviceDirty(pGEDevDesc dd); // GEdeviceDirty unused void GEdirtyDevice(pGEDevDesc dd); // GEdirtyDevice unused Rboolean GEcheckState(pGEDevDesc dd); // GEcheckState unused Rboolean GErecording(SEXP call, pGEDevDesc dd); // GErecording unused void GErecordGraphicOperation(SEXP op, SEXP args, pGEDevDesc dd); // GErecordGraphicOperation unused void GEinitDisplayList(pGEDevDesc dd); // GEinitDisplayList used 8 times in RSVGTipsDevice, Cairo, RSvgDevice, JavaGD, rvg, svglite void GEplayDisplayList(pGEDevDesc dd); // GEplayDisplayList used 5 times in Cairo, JavaGD, cairoDevice void GEcopyDisplayList(int fromDevice); // GEcopyDisplayList unused SEXP GEcreateSnapshot(pGEDevDesc dd); // GEcreateSnapshot used 1 times in Cairo void GEplaySnapshot(SEXP snapshot, pGEDevDesc dd); // GEplaySnapshot unused void GEonExit(void); // GEonExit unused void GEnullDevice(void); // GEnullDevice unused SEXP Rf_CreateAtVector(double*, double*, int, Rboolean); // Rf_CreateAtVector unused // CreateAtVector unused void Rf_GAxisPars(double *min, double *max, int *n, Rboolean log, int axis); // Rf_GAxisPars unused // GAxisPars unused } # /Users/ls/Source/git/fastr/com.oracle.truffle.r.native/include/R_ext/Lapack.h extern void ilaver_(int *major, int *minor, int *patch); // ilaver_ used 2 times in ltsk extern "C" { extern void dbdsqr_(const char* uplo, const int* n, const int* ncvt, const int* nru, const int* ncc, double* d, double* e, double* vt, const int* ldvt, double* u, const int* ldu, double* c, const int* ldc, double* work, int* info); extern void ddisna_(const char* job, const int* m, const int* n, double* d, double* sep, int* info); extern void dgbbrd_(const char* vect, const int* m, const int* n, const int* ncc, const int* kl, const int* ku, double* ab, const int* ldab, double* d, double* e, double* q, const int* ldq, double* Rf_pt, const int* ldpt, double* c, const int* ldc, double* work, int* info); extern void dgbcon_(const char* norm, const int* n, const int* kl, const int* ku, double* ab, const int* ldab, int* ipiv, const double* anorm, double* rcond, double* work, int* iwork, int* info); extern void dgbequ_(const int* m, const int* n, const int* kl, const int* ku, double* ab, const int* ldab, double* r, double* c, double* rowcnd, double* colcnd, double* amax, int* info); extern void dgbrfs_(const char* trans, const int* n, const int* kl, const int* ku, const int* nrhs, double* ab, const int* ldab, double* afb, const int* ldafb, int* ipiv, double* b, const int* ldb, double* x, const int* ldx, double* ferr, double* berr, double* work, int* iwork, int* info); extern void dgbsv_(const int* n, const int* kl,const int* ku, const int* nrhs, double* ab, const int* ldab, int* ipiv, double* b, const int* ldb, int* info); extern void dgbsvx_(const int* fact, const char* trans, const int* n, const int* kl,const int* ku, const int* nrhs, double* ab, const int* ldab, double* afb, const int* ldafb, int* ipiv, const char* equed, double* r, double* c, double* b, const int* ldb, double* x, const int* ldx, double* rcond, double* ferr, double* berr, double* work, int* iwork, int* info); extern void dgbtf2_(const int* m, const int* n, const int* kl,const int* ku, double* ab, const int* ldab, int* ipiv, int* info); extern void dgbtrf_(const int* m, const int* n, const int* kl,const int* ku, double* ab, const int* ldab, int* ipiv, int* info); extern void dgbtrs_(const char* trans, const int* n, const int* kl, const int* ku, const int* nrhs, const double* ab, const int* ldab, const int* ipiv, double* b, const int* ldb, int* info); extern void dgebak_(const char* job, const char* side, const int* n, const int* ilo, const int* ihi, double* scale, const int* m, double* v, const int* ldv, int* info); extern void dgebal_(const char* job, const int* n, double* a, const int* lda, int* ilo, int* ihi, double* scale, int* info); extern void dgebd2_(const int* m, const int* n, double* a, const int* lda, double* d, double* e, double* tauq, double* taup, double* work, int* info); extern void dgebrd_(const int* m, const int* n, double* a, const int* lda, double* d, double* e, double* tauq, double* taup, double* work, const int* lwork, int* info); extern void dgecon_(const char* norm, const int* n, const double* a, const int* lda, const double* anorm, double* rcond, double* work, int* iwork, int* info); extern void dgeequ_(const int* m, const int* n, double* a, const int* lda, double* r, double* c, double* rowcnd, double* colcnd, double* amax, int* info); extern void dgees_(const char* jobvs, const char* sort, int (*select)(const double*, const double*), const int* n, double* a, const int* lda, int* sdim, double* wr, double* wi, double* vs, const int* ldvs, double* work, const int* lwork, int* bwork, int* info); extern void dgeesx_(const char* jobvs, const char* sort, int (*select)(const double*, const double*), const char* sense, const int* n, double* a, const int* lda, int* sdim, double* wr, double* wi, double* vs, const int* ldvs, double* rconde, double* rcondv, double* work, const int* lwork, int* iwork, const int* liwork, int* bwork, int* info); extern void dgeev_(const char* jobvl, const char* jobvr, const int* n, double* a, const int* lda, double* wr, double* wi, double* vl, const int* ldvl, double* vr, const int* ldvr, double* work, const int* lwork, int* info); extern void dgeevx_(const char* balanc, const char* jobvl, const char* jobvr, const char* sense, const int* n, double* a, const int* lda, double* wr, double* wi, double* vl, const int* ldvl, double* vr, const int* ldvr, int* ilo, int* ihi, double* scale, double* abnrm, double* rconde, double* rcondv, double* work, const int* lwork, int* iwork, int* info); extern void dgegv_(const char* jobvl, const char* jobvr, const int* n, double* a, const int* lda, double* b, const int* ldb, double* alphar, double* alphai, const double* Rf_beta, double* vl, const int* ldvl, double* vr, const int* ldvr, double* work, const int* lwork, int* info); extern void dgehd2_(const int* n, const int* ilo, const int* ihi, double* a, const int* lda, double* tau, double* work, int* info); extern void dgehrd_(const int* n, const int* ilo, const int* ihi, double* a, const int* lda, double* tau, double* work, const int* lwork, int* info); extern void dgelq2_(const int* m, const int* n, double* a, const int* lda, double* tau, double* work, int* info); extern void dgelqf_(const int* m, const int* n, double* a, const int* lda, double* tau, double* work, const int* lwork, int* info); extern void dgels_(const char* trans, const int* m, const int* n, const int* nrhs, double* a, const int* lda, double* b, const int* ldb, double* work, const int* lwork, int* info); extern void dgelss_(const int* m, const int* n, const int* nrhs, double* a, const int* lda, double* b, const int* ldb, double* s, double* rcond, int* rank, double* work, const int* lwork, int* info); extern void dgelsy_(const int* m, const int* n, const int* nrhs, double* a, const int* lda, double* b, const int* ldb, int* jpvt, const double* rcond, int* rank, double* work, const int* lwork, int* info); extern void dgeql2_(const int* m, const int* n, double* a, const int* lda, double* tau, double* work, int* info); extern void dgeqlf_(const int* m, const int* n, double* a, const int* lda, double* tau, double* work, const int* lwork, int* info); extern void dgeqp3_(const int* m, const int* n, double* a, const int* lda, int* jpvt, double* tau, double* work, const int* lwork, int* info); extern void dgeqpf_(const int* m, const int* n, double* a, const int* lda, int* jpvt, double* tau, double* work, int* info); extern void dgeqr2_(const int* m, const int* n, double* a, const int* lda, double* tau, double* work, int* info); extern void dgeqrf_(const int* m, const int* n, double* a, const int* lda, double* tau, double* work, const int* lwork, int* info); extern void dgerfs_(const char* trans, const int* n, const int* nrhs, double* a, const int* lda, double* af, const int* ldaf, int* ipiv, double* b, const int* ldb, double* x, const int* ldx, double* ferr, double* berr, double* work, int* iwork, int* info); extern void dgerq2_(const int* m, const int* n, double* a, const int* lda, double* tau, double* work, int* info); extern void dgerqf_(const int* m, const int* n, double* a, const int* lda, double* tau, double* work, const int* lwork, int* info); extern void dgesv_(const int* n, const int* nrhs, double* a, const int* lda, int* ipiv, double* b, const int* ldb, int* info); extern void dgesvd_(const char* jobu, const char* jobvt, const int* m, const int* n, double* a, const int* lda, double* s, double* u, const int* ldu, double* vt, const int* ldvt, double* work, const int* lwork, int* info); extern void dgesvx_(const char* fact, const char* trans, const int* n, const int* nrhs, double* a, const int* lda, double* af, const int* ldaf, int* ipiv, char *equed, double* r, double* c, double* b, const int* ldb, double* x, const int* ldx, double* rcond, double* ferr, double* berr, double* work, int* iwork, int* info); extern void dgetf2_(const int* m, const int* n, double* a, const int* lda, int* ipiv, int* info); extern void dgetrf_(const int* m, const int* n, double* a, const int* lda, int* ipiv, int* info); extern void dgetri_(const int* n, double* a, const int* lda, int* ipiv, double* work, const int* lwork, int* info); extern void dgetrs_(const char* trans, const int* n, const int* nrhs, const double* a, const int* lda, const int* ipiv, double* b, const int* ldb, int* info); extern void dggbak_(const char* job, const char* side, const int* n, const int* ilo, const int* ihi, double* lscale, double* rscale, const int* m, double* v, const int* ldv, int* info); extern void dggbal_(const char* job, const int* n, double* a, const int* lda, double* b, const int* ldb, int* ilo, int* ihi, double* lscale, double* rscale, double* work, int* info); extern void dgges_(const char* jobvsl, const char* jobvsr, const char* sort, int (*delztg)(double*, double*, double*), const int* n, double* a, const int* lda, double* b, const int* ldb, double* alphar, double* alphai, const double* Rf_beta, double* vsl, const int* ldvsl, double* vsr, const int* ldvsr, double* work, const int* lwork, int* bwork, int* info); extern void dggglm_(const int* n, const int* m, const int* p, double* a, const int* lda, double* b, const int* ldb, double* d, double* x, double* y, double* work, const int* lwork, int* info); extern void dgghrd_(const char* compq, const char* compz, const int* n, const int* ilo, const int* ihi, double* a, const int* lda, double* b, const int* ldb, double* q, const int* ldq, double* z, const int* ldz, int* info); extern void dgglse_(const int* m, const int* n, const int* p, double* a, const int* lda, double* b, const int* ldb, double* c, double* d, double* x, double* work, const int* lwork, int* info); extern void dggqrf_(const int* n, const int* m, const int* p, double* a, const int* lda, double* taua, double* b, const int* ldb, double* taub, double* work, const int* lwork, int* info); extern void dggrqf_(const int* m, const int* p, const int* n, double* a, const int* lda, double* taua, double* b, const int* ldb, double* taub, double* work, const int* lwork, int* info); extern void dggsvd_(const char* jobu, const char* jobv, const char* jobq, const int* m, const int* n, const int* p, const int* k, const int* l, double* a, const int* lda, double* b, const int* ldb, const double* alpha, const double* Rf_beta, double* u, const int* ldu, double* v, const int* ldv, double* q, const int* ldq, double* work, int* iwork, int* info); extern void dgtcon_(const char* norm, const int* n, double* dl, double* d, double* du, double* du2, int* ipiv, const double* anorm, double* rcond, double* work, int* iwork, int* info); extern void dgtrfs_(const char* trans, const int* n, const int* nrhs, double* dl, double* d, double* du, double* dlf, double* Rf_df, double* duf, double* du2, int* ipiv, double* b, const int* ldb, double* x, const int* ldx, double* ferr, double* berr, double* work, int* iwork, int* info); extern void dgtsv_(const int* n, const int* nrhs, double* dl, double* d, double* du, double* b, const int* ldb, int* info); extern void dgtsvx_(const int* fact, const char* trans, const int* n, const int* nrhs, double* dl, double* d, double* du, double* dlf, double* Rf_df, double* duf, double* du2, int* ipiv, double* b, const int* ldb, double* x, const int* ldx, double* rcond, double* ferr, double* berr, double* work, int* iwork, int* info); extern void dgttrf_(const int* n, double* dl, double* d, double* du, double* du2, int* ipiv, int* info); extern void dgttrs_(const char* trans, const int* n, const int* nrhs, double* dl, double* d, double* du, double* du2, int* ipiv, double* b, const int* ldb, int* info); extern void dopgtr_(const char* uplo, const int* n, const double* ap, const double* tau, double* q, const int* ldq, double* work, int* info); extern void dopmtr_(const char* side, const char* uplo, const char* trans, const int* m, const int* n, const double* ap, const double* tau, double* c, const int* ldc, double* work, int* info); extern void dorg2l_(const int* m, const int* n, const int* k, double* a, const int* lda, const double* tau, double* work, int* info); extern void dorg2r_(const int* m, const int* n, const int* k, double* a, const int* lda, const double* tau, double* work, int* info); extern void dorgbr_(const char* vect, const int* m, const int* n, const int* k, double* a, const int* lda, const double* tau, double* work, const int* lwork, int* info); extern void dorghr_(const int* n, const int* ilo, const int* ihi, double* a, const int* lda, const double* tau, double* work, const int* lwork, int* info); extern void dorgl2_(const int* m, const int* n, const int* k, double* a, const int* lda, const double* tau, double* work, int* info); extern void dorglq_(const int* m, const int* n, const int* k, double* a, const int* lda, const double* tau, double* work, const int* lwork, int* info); extern void dorgql_(const int* m, const int* n, const int* k, double* a, const int* lda, const double* tau, double* work, const int* lwork, int* info); extern void dorgqr_(const int* m, const int* n, const int* k, double* a, const int* lda, const double* tau, double* work, const int* lwork, int* info); extern void dorgr2_(const int* m, const int* n, const int* k, double* a, const int* lda, const double* tau, double* work, int* info); extern void dorgrq_(const int* m, const int* n, const int* k, double* a, const int* lda, const double* tau, double* work, const int* lwork, int* info); extern void dorgtr_(const char* uplo, const int* n, double* a, const int* lda, const double* tau, double* work, const int* lwork, int* info); extern void dorm2l_(const char* side, const char* trans, const int* m, const int* n, const int* k, const double* a, const int* lda, const double* tau, double* c, const int* ldc, double* work, int* info); extern void dorm2r_(const char* side, const char* trans, const int* m, const int* n, const int* k, const double* a, const int* lda, const double* tau, double* c, const int* ldc, double* work, int* info); extern void dormbr_(const char* vect, const char* side, const char* trans, const int* m, const int* n, const int* k, const double* a, const int* lda, const double* tau, double* c, const int* ldc, double* work, const int* lwork, int* info); extern void dormhr_(const char* side, const char* trans, const int* m, const int* n, const int* ilo, const int* ihi, const double* a, const int* lda, const double* tau, double* c, const int* ldc, double* work, const int* lwork, int* info); extern void dorml2_(const char* side, const char* trans, const int* m, const int* n, const int* k, const double* a, const int* lda, const double* tau, double* c, const int* ldc, double* work, int* info); extern void dormlq_(const char* side, const char* trans, const int* m, const int* n, const int* k, const double* a, const int* lda, const double* tau, double* c, const int* ldc, double* work, const int* lwork, int* info); extern void dormql_(const char* side, const char* trans, const int* m, const int* n, const int* k, const double* a, const int* lda, const double* tau, double* c, const int* ldc, double* work, const int* lwork, int* info); extern void dormqr_(const char* side, const char* trans, const int* m, const int* n, const int* k, const double* a, const int* lda, const double* tau, double* c, const int* ldc, double* work, const int* lwork, int* info); extern void dormr2_(const char* side, const char* trans, const int* m, const int* n, const int* k, const double* a, const int* lda, const double* tau, double* c, const int* ldc, double* work, int* info); extern void dormrq_(const char* side, const char* trans, const int* m, const int* n, const int* k, const double* a, const int* lda, const double* tau, double* c, const int* ldc, double* work, const int* lwork, int* info); extern void dormtr_(const char* side, const char* uplo, const char* trans, const int* m, const int* n, const double* a, const int* lda, const double* tau, double* c, const int* ldc, double* work, const int* lwork, int* info); extern void dpbcon_(const char* uplo, const int* n, const int* kd, const double* ab, const int* ldab, const double* anorm, double* rcond, double* work, int* iwork, int* info); extern void dpbequ_(const char* uplo, const int* n, const int* kd, const double* ab, const int* ldab, double* s, double* scond, double* amax, int* info); extern void dpbrfs_(const char* uplo, const int* n, const int* kd, const int* nrhs, const double* ab, const int* ldab, const double* afb, const int* ldafb, const double* b, const int* ldb, double* x, const int* ldx, double* ferr, double* berr, double* work, int* iwork, int* info); extern void dpbstf_(const char* uplo, const int* n, const int* kd, double* ab, const int* ldab, int* info); extern void dpbsv_(const char* uplo, const int* n, const int* kd, const int* nrhs, double* ab, const int* ldab, double* b, const int* ldb, int* info); extern void dpbsvx_(const int* fact, const char* uplo, const int* n, const int* kd, const int* nrhs, double* ab, const int* ldab, double* afb, const int* ldafb, char* equed, double* s, double* b, const int* ldb, double* x, const int* ldx, double* rcond, double* ferr, double* berr, double* work, int* iwork, int* info); extern void dpbtf2_(const char* uplo, const int* n, const int* kd, double* ab, const int* ldab, int* info); extern void dpbtrf_(const char* uplo, const int* n, const int* kd, double* ab, const int* ldab, int* info); extern void dpbtrs_(const char* uplo, const int* n, const int* kd, const int* nrhs, const double* ab, const int* ldab, double* b, const int* ldb, int* info); extern void dpocon_(const char* uplo, const int* n, const double* a, const int* lda, const double* anorm, double* rcond, double* work, int* iwork, int* info); extern void dpoequ_(const int* n, const double* a, const int* lda, double* s, double* scond, double* amax, int* info); extern void dporfs_(const char* uplo, const int* n, const int* nrhs, const double* a, const int* lda, const double* af, const int* ldaf, const double* b, const int* ldb, double* x, const int* ldx, double* ferr, double* berr, double* work, int* iwork, int* info); extern void dposv_(const char* uplo, const int* n, const int* nrhs, double* a, const int* lda, double* b, const int* ldb, int* info); extern void dposvx_(const int* fact, const char* uplo, const int* n, const int* nrhs, double* a, const int* lda, double* af, const int* ldaf, char* equed, double* s, double* b, const int* ldb, double* x, const int* ldx, double* rcond, double* ferr, double* berr, double* work, int* iwork, int* info); extern void dpotf2_(const char* uplo, const int* n, double* a, const int* lda, int* info); extern void dpotrf_(const char* uplo, const int* n, double* a, const int* lda, int* info); extern void dpotri_(const char* uplo, const int* n, double* a, const int* lda, int* info); extern void dpotrs_(const char* uplo, const int* n, const int* nrhs, const double* a, const int* lda, double* b, const int* ldb, int* info); extern void dppcon_(const char* uplo, const int* n, const double* ap, const double* anorm, double* rcond, double* work, int* iwork, int* info); extern void dppequ_(const char* uplo, const int* n, const double* ap, double* s, double* scond, double* amax, int* info); extern void dpprfs_(const char* uplo, const int* n, const int* nrhs, const double* ap, const double* afp, const double* b, const int* ldb, double* x, const int* ldx, double* ferr, double* berr, double* work, int* iwork, int* info); extern void dppsv_(const char* uplo, const int* n, const int* nrhs, const double* ap, double* b, const int* ldb, int* info); extern void dppsvx_(const int* fact, const char* uplo, const int* n, const int* nrhs, double* ap, double* afp, char* equed, double* s, double* b, const int* ldb, double* x, const int* ldx, double* rcond, double* ferr, double* berr, double* work, int* iwork, int* info); extern void dpptrf_(const char* uplo, const int* n, double* ap, int* info); extern void dpptri_(const char* uplo, const int* n, double* ap, int* info); extern void dpptrs_(const char* uplo, const int* n, const int* nrhs, const double* ap, double* b, const int* ldb, int* info); extern void dptcon_(const int* n, const double* d, const double* e, const double* anorm, double* rcond, double* work, int* info); extern void dpteqr_(const char* compz, const int* n, double* d, double* e, double* z, const int* ldz, double* work, int* info); extern void dptrfs_(const int* n, const int* nrhs, const double* d, const double* e, const double* Rf_df, const double* ef, const double* b, const int* ldb, double* x, const int* ldx, double* ferr, double* berr, double* work, int* info); extern void dptsv_(const int* n, const int* nrhs, double* d, double* e, double* b, const int* ldb, int* info); extern void dptsvx_(const int* fact, const int* n, const int* nrhs, const double* d, const double* e, double* Rf_df, double* ef, const double* b, const int* ldb, double* x, const int* ldx, double* rcond, double* ferr, double* berr, double* work, int* info); extern void dpttrf_(const int* n, double* d, double* e, int* info); extern void dpttrs_(const int* n, const int* nrhs, const double* d, const double* e, double* b, const int* ldb, int* info); extern void drscl_(const int* n, const double* da, double* x, const int* incx); extern void dsbev_(const char* jobz, const char* uplo, const int* n, const int* kd, double* ab, const int* ldab, double* w, double* z, const int* ldz, double* work, int* info); extern void dsbevd_(const char* jobz, const char* uplo, const int* n, const int* kd, double* ab, const int* ldab, double* w, double* z, const int* ldz, double* work, const int* lwork, int* iwork, const int* liwork, int* info); extern void dsbevx_(const char* jobz, const char* range, const char* uplo, const int* n, const int* kd, double* ab, const int* ldab, double* q, const int* ldq, const double* vl, const double* vu, const int* il, const int* iu, const double* abstol, int* m, double* w, double* z, const int* ldz, double* work, int* iwork, int* ifail, int* info); extern void dsbgst_(const char* vect, const char* uplo, const int* n, const int* ka, const int* kb, double* ab, const int* ldab, double* bb, const int* ldbb, double* x, const int* ldx, double* work, int* info); extern void dsbgv_(const char* jobz, const char* uplo, const int* n, const int* ka, const int* kb, double* ab, const int* ldab, double* bb, const int* ldbb, double* w, double* z, const int* ldz, double* work, int* info); extern void dsbtrd_(const char* vect, const char* uplo, const int* n, const int* kd, double* ab, const int* ldab, double* d, double* e, double* q, const int* ldq, double* work, int* info); extern void dspcon_(const char* uplo, const int* n, const double* ap, const int* ipiv, const double* anorm, double* rcond, double* work, int* iwork, int* info); extern void dspev_(const char* jobz, const char* uplo, const int* n, double* ap, double* w, double* z, const int* ldz, double* work, int* info); extern void dspevd_(const char* jobz, const char* uplo, const int* n, double* ap, double* w, double* z, const int* ldz, double* work, const int* lwork, int* iwork, const int* liwork, int* info); extern void dspevx_(const char* jobz, const char* range, const char* uplo, const int* n, double* ap, const double* vl, const double* vu, const int* il, const int* iu, const double* abstol, int* m, double* w, double* z, const int* ldz, double* work, int* iwork, int* ifail, int* info); extern void dspgst_(const int* itype, const char* uplo, const int* n, double* ap, double* bp, int* info); extern void dspgv_(const int* itype, const char* jobz, const char* uplo, const int* n, double* ap, double* bp, double* w, double* z, const int* ldz, double* work, int* info); extern void dsprfs_(const char* uplo, const int* n, const int* nrhs, const double* ap, const double* afp, const int* ipiv, const double* b, const int* ldb, double* x, const int* ldx, double* ferr, double* berr, double* work, int* iwork, int* info); extern void dspsv_(const char* uplo, const int* n, const int* nrhs, double* ap, int* ipiv, double* b, const int* ldb, int* info); extern void dspsvx_(const int* fact, const char* uplo, const int* n, const int* nrhs, const double* ap, double* afp, int* ipiv, const double* b, const int* ldb, double* x, const int* ldx, double* rcond, double* ferr, double* berr, double* work, int* iwork, int* info); extern void dsptrd_(const char* uplo, const int* n, double* ap, double* d, double* e, double* tau, int* info); extern void dsptrf_(const char* uplo, const int* n, double* ap, int* ipiv, int* info); extern void dsptri_(const char* uplo, const int* n, double* ap, const int* ipiv, double* work, int* info); extern void dsptrs_(const char* uplo, const int* n, const int* nrhs, const double* ap, const int* ipiv, double* b, const int* ldb, int* info); extern void dstebz_(const char* range, const char* order, const int* n, const double* vl, const double* vu, const int* il, const int* iu, const double *abstol, const double* d, const double* e, int* m, int* nsplit, double* w, int* iblock, int* isplit, double* work, int* iwork, int* info); extern void dstedc_(const char* compz, const int* n, double* d, double* e, double* z, const int* ldz, double* work, const int* lwork, int* iwork, const int* liwork, int* info); extern void dstein_(const int* n, const double* d, const double* e, const int* m, const double* w, const int* iblock, const int* isplit, double* z, const int* ldz, double* work, int* iwork, int* ifail, int* info); extern void dsteqr_(const char* compz, const int* n, double* d, double* e, double* z, const int* ldz, double* work, int* info); extern void dsterf_(const int* n, double* d, double* e, int* info); extern void dstev_(const char* jobz, const int* n, double* d, double* e, double* z, const int* ldz, double* work, int* info); extern void dstevd_(const char* jobz, const int* n, double* d, double* e, double* z, const int* ldz, double* work, const int* lwork, int* iwork, const int* liwork, int* info); extern void dstevx_(const char* jobz, const char* range, const int* n, double* d, double* e, const double* vl, const double* vu, const int* il, const int* iu, const double* abstol, int* m, double* w, double* z, const int* ldz, double* work, int* iwork, int* ifail, int* info); extern void dsycon_(const char* uplo, const int* n, const double* a, const int* lda, const int* ipiv, const double* anorm, double* rcond, double* work, int* iwork, int* info); extern void dsyev_(const char* jobz, const char* uplo, const int* n, double* a, const int* lda, double* w, double* work, const int* lwork, int* info); extern void dsyevd_(const char* jobz, const char* uplo, const int* n, double* a, const int* lda, double* w, double* work, const int* lwork, int* iwork, const int* liwork, int* info); extern void dsyevx_(const char* jobz, const char* range, const char* uplo, const int* n, double* a, const int* lda, const double* vl, const double* vu, const int* il, const int* iu, const double* abstol, int* m, double* w, double* z, const int* ldz, double* work, const int* lwork, int* iwork, int* ifail, int* info); extern void dsyevr_(const char *jobz, const char *range, const char *uplo, const int *n, double *a, const int *lda, const double *vl, const double *vu, const int *il, const int *iu, const double *abstol, int *m, double *w, double *z, const int *ldz, int *isuppz, double *work, const int *lwork, int *iwork, const int *liwork, int *info); extern void dsygs2_(const int* itype, const char* uplo, const int* n, double* a, const int* lda, const double* b, const int* ldb, int* info); extern void dsygst_(const int* itype, const char* uplo, const int* n, double* a, const int* lda, const double* b, const int* ldb, int* info); extern void dsygv_(const int* itype, const char* jobz, const char* uplo, const int* n, double* a, const int* lda, double* b, const int* ldb, double* w, double* work, const int* lwork, int* info); extern void dsyrfs_(const char* uplo, const int* n, const int* nrhs, const double* a, const int* lda, const double* af, const int* ldaf, const int* ipiv, const double* b, const int* ldb, double* x, const int* ldx, double* ferr, double* berr, double* work, int* iwork, int* info); extern void dsysv_(const char* uplo, const int* n, const int* nrhs, double* a, const int* lda, int* ipiv, double* b, const int* ldb, double* work, const int* lwork, int* info); extern void dsysvx_(const int* fact, const char* uplo, const int* n, const int* nrhs, const double* a, const int* lda, double* af, const int* ldaf, int* ipiv, const double* b, const int* ldb, double* x, const int* ldx, double* rcond, double* ferr, double* berr, double* work, const int* lwork, int* iwork, int* info); extern void dsytd2_(const char* uplo, const int* n, double* a, const int* lda, double* d, double* e, double* tau, int* info); extern void dsytf2_(const char* uplo, const int* n, double* a, const int* lda, int* ipiv, int* info); extern void dsytrd_(const char* uplo, const int* n, double* a, const int* lda, double* d, double* e, double* tau, double* work, const int* lwork, int* info); extern void dsytrf_(const char* uplo, const int* n, double* a, const int* lda, int* ipiv, double* work, const int* lwork, int* info); extern void dsytri_(const char* uplo, const int* n, double* a, const int* lda, const int* ipiv, double* work, int* info); extern void dsytrs_(const char* uplo, const int* n, const int* nrhs, const double* a, const int* lda, const int* ipiv, double* b, const int* ldb, int* info); extern void dtbcon_(const char* norm, const char* uplo, const char* diag, const int* n, const int* kd, const double* ab, const int* ldab, double* rcond, double* work, int* iwork, int* info); extern void dtbrfs_(const char* uplo, const char* trans, const char* diag, const int* n, const int* kd, const int* nrhs, const double* ab, const int* ldab, const double* b, const int* ldb, double* x, const int* ldx, double* ferr, double* berr, double* work, int* iwork, int* info); extern void dtbtrs_(const char* uplo, const char* trans, const char* diag, const int* n, const int* kd, const int* nrhs, const double* ab, const int* ldab, double* b, const int* ldb, int* info); extern void dtgevc_(const char* side, const char* howmny, const int* select, const int* n, const double* a, const int* lda, const double* b, const int* ldb, double* vl, const int* ldvl, double* vr, const int* ldvr, const int* mm, int* m, double* work, int* info); extern void dtgsja_(const char* jobu, const char* jobv, const char* jobq, const int* m, const int* p, const int* n, const int* k, const int* l, double* a, const int* lda, double* b, const int* ldb, const double* tola, const double* tolb, double* alpha, double* Rf_beta, double* u, const int* ldu, double* v, const int* ldv, double* q, const int* ldq, double* work, int* ncycle, int* info); extern void dtpcon_(const char* norm, const char* uplo, const char* diag, const int* n, const double* ap, double* rcond, double* work, int* iwork, int* info); extern void dtprfs_(const char* uplo, const char* trans, const char* diag, const int* n, const int* nrhs, const double* ap, const double* b, const int* ldb, double* x, const int* ldx, double* ferr, double* berr, double* work, int* iwork, int* info); extern void dtptri_(const char* uplo, const char* diag, const int* n, double* ap, int* info); extern void dtptrs_(const char* uplo, const char* trans, const char* diag, const int* n, const int* nrhs, const double* ap, double* b, const int* ldb, int* info); extern void dtrcon_(const char* norm, const char* uplo, const char* diag, const int* n, const double* a, const int* lda, double* rcond, double* work, int* iwork, int* info); extern void dtrevc_(const char* side, const char* howmny, const int* select, const int* n, const double* t, const int* ldt, double* vl, const int* ldvl, double* vr, const int* ldvr, const int* mm, int* m, double* work, int* info); extern void dtrexc_(const char* compq, const int* n, double* t, const int* ldt, double* q, const int* ldq, int* ifst, int* ILST, double* work, int* info); extern void dtrrfs_(const char* uplo, const char* trans, const char* diag, const int* n, const int* nrhs, const double* a, const int* lda, const double* b, const int* ldb, double* x, const int* ldx, double* ferr, double* berr, double* work, int* iwork, int* info); extern void dtrsen_(const char* job, const char* compq, const int* select, const int* n, double* t, const int* ldt, double* q, const int* ldq, double* wr, double* wi, int* m, double* s, double* sep, double* work, const int* lwork, int* iwork, const int* liwork, int* info); extern void dtrsna_(const char* job, const char* howmny, const int* select, const int* n, const double* t, const int* ldt, const double* vl, const int* ldvl, const double* vr, const int* ldvr, double* s, double* sep, const int* mm, int* m, double* work, const int* lwork, int* iwork, int* info); extern void dtrsyl_(const char* trana, const char* tranb, const int* isgn, const int* m, const int* n, const double* a, const int* lda, const double* b, const int* ldb, double* c, const int* ldc, double* scale, int* info); extern void dtrti2_(const char* uplo, const char* diag, const int* n, double* a, const int* lda, int* info); extern void dtrtri_(const char* uplo, const char* diag, const int* n, double* a, const int* lda, int* info); extern void dtrtrs_(const char* uplo, const char* trans, const char* diag, const int* n, const int* nrhs, const double* a, const int* lda, double* b, const int* ldb, int* info); extern void dtzrqf_(const int* m, const int* n, double* a, const int* lda, double* tau, int* info); extern void dhgeqz_(const char* job, const char* compq, const char* compz, const int* n, const int *ILO, const int* IHI, double* a, const int* lda, double* b, const int* ldb, double* alphar, double* alphai, const double* Rf_beta, double* q, const int* ldq, double* z, const int* ldz, double* work, const int* lwork, int* info); extern void dhsein_(const char* side, const char* eigsrc, const char* initv, int* select, const int* n, double* h, const int* ldh, double* wr, double* wi, double* vl, const int* ldvl, double* vr, const int* ldvr, const int* mm, int* m, double* work, int* ifaill, int* ifailr, int* info); extern void dhseqr_(const char* job, const char* compz, const int* n, const int* ilo, const int* ihi, double* h, const int* ldh, double* wr, double* wi, double* z, const int* ldz, double* work, const int* lwork, int* info); extern void dlabad_(double* small, double* large); extern void dlabrd_(const int* m, const int* n, const int* nb, double* a, const int* lda, double* d, double* e, double* tauq, double* taup, double* x, const int* ldx, double* y, const int* ldy); extern void dlacon_(const int* n, double* v, double* x, int* isgn, double* est, int* kase); extern void dlacpy_(const char* uplo, const int* m, const int* n, const double* a, const int* lda, double* b, const int* ldb); extern void dladiv_(const double* a, const double* b, const double* c, const double* d, double* p, double* q); extern void dlae2_(const double* a, const double* b, const double* c, double* rt1, double* rt2); extern void dlaebz_(const int* ijob, const int* nitmax, const int* n, const int* mmax, const int* minp, const int* nbmin, const double* abstol, const double* reltol, const double* pivmin, double* d, double* e, double* e2, int* nval, double* ab, double* c, int* mout, int* nab, double* work, int* iwork, int* info); extern void dlaed0_(const int* icompq, const int* qsiz, const int* n, double* d, double* e, double* q, const int* ldq, double* qstore, const int* ldqs, double* work, int* iwork, int* info); extern void dlaed1_(const int* n, double* d, double* q, const int* ldq, int* indxq, const double* rho, const int* cutpnt, double* work, int* iwork, int* info); extern void dlaed2_(const int* k, const int* n, double* d, double* q, const int* ldq, int* indxq, double* rho, double* z, double* dlamda, double* w, double* q2, int* indx, int* indxc, int* indxp, int* coltyp, int* info); extern void dlaed3_(const int* k, const int* n, const int* n1, double* d, double* q, const int* ldq, const double* rho, double* dlamda, double* q2, int* indx, int* ctot, double* w, double* s, int* info); extern void dlaed4_(const int* n, const int* i, const double* d, const double* z, const double* delta, const double* rho, double* dlam, int* info); extern void dlaed5_(const int* i, const double* d, const double* z, double* delta, const double* rho, double* dlam); extern void dlaed6_(const int* kniter, const int* orgati, const double* rho, const double* d, const double* z, const double* finit, double* tau, int* info); extern void dlaed7_(const int* icompq, const int* n, const int* qsiz, const int* tlvls, const int* curlvl, const int* curpbm, double* d, double* q, const int* ldq, int* indxq, const double* rho, const int* cutpnt, double* qstore, double* qptr, const int* prmptr, const int* perm, const int* givptr, const int* givcol, const double* givnum, double* work, int* iwork, int* info); extern void dlaed8_(const int* icompq, const int* k, const int* n, const int* qsiz, double* d, double* q, const int* ldq, const int* indxq, double* rho, const int* cutpnt, const double* z, double* dlamda, double* q2, const int* ldq2, double* w, int* perm, int* givptr, int* givcol, double* givnum, int* indxp, int* indx, int* info); extern void dlaed9_(const int* k, const int* kstart, const int* kstop, const int* n, double* d, double* q, const int* ldq, const double* rho, const double* dlamda, const double* w, double* s, const int* lds, int* info); extern void dlaeda_(const int* n, const int* tlvls, const int* curlvl, const int* curpbm, const int* prmptr, const int* perm, const int* givptr, const int* givcol, const double* givnum, const double* q, const int* qptr, double* z, double* ztemp, int* info); extern void dlaein_(const int* rightv, const int* noinit, const int* n, const double* h, const int* ldh, const double* wr, const double* wi, double* vr, double* vi, double* b, const int* ldb, double* work, const double* eps3, const double* smlnum, const double* bignum, int* info); extern void dlaev2_(const double* a, const double* b, const double* c, double* rt1, double* rt2, double* cs1, double *sn1); extern void dlaexc_(const int* wantq, const int* n, double* t, const int* ldt, double* q, const int* ldq, const int* j1, const int* n1, const int* n2, double* work, int* info); extern void dlag2_(const double* a, const int* lda, const double* b, const int* ldb, const double* safmin, double* scale1, double* scale2, double* wr1, double* wr2, double* wi); extern void dlags2_(const int* upper, const double* a1, const double* a2, const double* a3, const double* b1, const double* b2, const double* b3, double* csu, double* snu, double* csv, double* snv, double *csq, double *snq); extern void dlagtf_(const int* n, double* a, const double* lambda, double* b, double* c, const double *tol, double* d, int* in, int* info); extern void dlagtm_(const char* trans, const int* n, const int* nrhs, const double* alpha, const double* dl, const double* d, const double* du, const double* x, const int* ldx, const double* Rf_beta, double* b, const int* ldb); extern void dlagts_(const int* job, const int* n, const double* a, const double* b, const double* c, const double* d, const int* in, double* y, double* tol, int* info); extern void dlahqr_(const int* wantt, const int* wantz, const int* n, const int* ilo, const int* ihi, double* H, const int* ldh, double* wr, double* wi, const int* iloz, const int* ihiz, double* z, const int* ldz, int* info); extern void dlahrd_(const int* n, const int* k, const int* nb, double* a, const int* lda, double* tau, double* t, const int* ldt, double* y, const int* ldy); extern void dlaic1_(const int* job, const int* j, const double* x, const double* sest, const double* w, const double* gamma, double* sestpr, double* s, double* c); extern void dlaln2_(const int* ltrans, const int* na, const int* nw, const double* smin, const double* ca, const double* a, const int* lda, const double* d1, const double* d2, const double* b, const int* ldb, const double* wr, const double* wi, double* x, const int* ldx, double* scale, double* xnorm, int* info); extern double dlamch_(const char* cmach); extern void dlamrg_(const int* n1, const int* n2, const double* a, const int* dtrd1, const int* dtrd2, int* index); extern double dlangb_(const char* norm, const int* n, const int* kl, const int* ku, const double* ab, const int* ldab, double* work); extern double dlange_(const char* norm, const int* m, const int* n, const double* a, const int* lda, double* work); extern double dlangt_(const char* norm, const int* n, const double* dl, const double* d, const double* du); extern double dlanhs_(const char* norm, const int* n, const double* a, const int* lda, double* work); extern double dlansb_(const char* norm, const char* uplo, const int* n, const int* k, const double* ab, const int* ldab, double* work); extern double dlansp_(const char* norm, const char* uplo, const int* n, const double* ap, double* work); extern double dlanst_(const char* norm, const int* n, const double* d, const double* e); extern double dlansy_(const char* norm, const char* uplo, const int* n, const double* a, const int* lda, double* work); extern double dlantb_(const char* norm, const char* uplo, const char* diag, const int* n, const int* k, const double* ab, const int* ldab, double* work); extern double dlantp_(const char* norm, const char* uplo, const char* diag, const int* n, const double* ap, double* work); extern double dlantr_(const char* norm, const char* uplo, const char* diag, const int* m, const int* n, const double* a, const int* lda, double* work); extern void dlanv2_(double* a, double* b, double* c, double* d, double* rt1r, double* rt1i, double* rt2r, double* rt2i, double* cs, double *sn); extern void dlapll_(const int* n, double* x, const int* incx, double* y, const int* incy, double* ssmin); extern void dlapmt_(const int* forwrd, const int* m, const int* n, double* x, const int* ldx, const int* k); extern double dlapy2_(const double* x, const double* y); extern double dlapy3_(const double* x, const double* y, const double* z); extern void dlaqgb_(const int* m, const int* n, const int* kl, const int* ku, double* ab, const int* ldab, double* r, double* c, double* rowcnd, double* colcnd, const double* amax, char* equed); extern void dlaqge_(const int* m, const int* n, double* a, const int* lda, double* r, double* c, double* rowcnd, double* colcnd, const double* amax, char* equed); extern void dlaqsb_(const char* uplo, const int* n, const int* kd, double* ab, const int* ldab, const double* s, const double* scond, const double* amax, char* equed); extern void dlaqsp_(const char* uplo, const int* n, double* ap, const double* s, const double* scond, const double* amax, int* equed); extern void dlaqsy_(const char* uplo, const int* n, double* a, const int* lda, const double* s, const double* scond, const double* amax, int* equed); extern void dlaqtr_(const int* ltran, const int* lreal, const int* n, const double* t, const int* ldt, const double* b, const double* w, double* scale, double* x, double* work, int* info); extern void dlar2v_(const int* n, double* x, double* y, double* z, const int* incx, const double* c, const double* s, const int* incc); extern void dlarf_(const char* side, const int* m, const int* n, const double* v, const int* incv, const double* tau, double* c, const int* ldc, double* work); extern void dlarfb_(const char* side, const char* trans, const char* direct, const char* storev, const int* m, const int* n, const int* k, const double* v, const int* ldv, const double* t, const int* ldt, double* c, const int* ldc, double* work, const int* lwork); extern void dlarfg_(const int* n, const double* alpha, double* x, const int* incx, double* tau); extern void dlarft_(const char* direct, const char* storev, const int* n, const int* k, double* v, const int* ldv, const double* tau, double* t, const int* ldt); extern void dlarfx_(const char* side, const int* m, const int* n, const double* v, const double* tau, double* c, const int* ldc, double* work); extern void dlargv_(const int* n, double* x, const int* incx, double* y, const int* incy, double* c, const int* incc); extern void dlarnv_(const int* idist, int* iseed, const int* n, double* x); extern void dlartg_(const double* f, const double* g, double* cs, double* sn, double *r); extern void dlartv_(const int* n, double* x, const int* incx, double* y, const int* incy, const double* c, const double* s, const int* incc); extern void dlaruv_(int* iseed, const int* n, double* x); extern void dlas2_(const double* f, const double* g, const double* h, double* ssmin, double* ssmax); extern void dlascl_(const char* type, const int* kl,const int* ku, double* cfrom, double* cto, const int* m, const int* n, double* a, const int* lda, int* info); extern void dlaset_(const char* uplo, const int* m, const int* n, const double* alpha, const double* Rf_beta, double* a, const int* lda); extern void dlasq1_(const int* n, double* d, double* e, double* work, int* info); extern void dlasq2_(const int* m, double* q, double* e, double* qq, double* ee, const double* eps, const double* tol2, const double* small2, double* sup, int* kend, int* info); extern void dlasq3_(int* n, double* q, double* e, double* qq, double* ee, double* sup, double *sigma, int* kend, int* off, int* iphase, const int* iconv, const double* eps, const double* tol2, const double* small2); extern void dlasq4_(const int* n, const double* q, const double* e, double* tau, double* sup); extern void dlasr_(const char* side, const char* pivot, const char* direct, const int* m, const int* n, const double* c, const double* s, double* a, const int* lda); extern void dlasrt_(const char* id, const int* n, double* d, int* info); extern void dlassq_(const int* n, const double* x, const int* incx, double* scale, double* sumsq); extern void dlasv2_(const double* f, const double* g, const double* h, double* ssmin, double* ssmax, double* snr, double* csr, double* snl, double* csl); extern void dlaswp_(const int* n, double* a, const int* lda, const int* k1, const int* k2, const int* ipiv, const int* incx); extern void dlasy2_(const int* ltranl, const int* ltranr, const int* isgn, const int* n1, const int* n2, const double* tl, const int* ldtl, const double* tr, const int* ldtr, const double* b, const int* ldb, double* scale, double* x, const int* ldx, double* xnorm, int* info); extern void dlasyf_(const char* uplo, const int* n, const int* nb, const int* kb, double* a, const int* lda, int* ipiv, double* w, const int* ldw, int* info); extern void dlatbs_(const char* uplo, const char* trans, const char* diag, const char* normin, const int* n, const int* kd, const double* ab, const int* ldab, double* x, double* scale, double* cnorm, int* info); extern void dlatps_(const char* uplo, const char* trans, const char* diag, const char* normin, const int* n, const double* ap, double* x, double* scale, double* cnorm, int* info); extern void dlatrd_(const char* uplo, const int* n, const int* nb, double* a, const int* lda, double* e, double* tau, double* w, const int* ldw); extern void dlatrs_(const char* uplo, const char* trans, const char* diag, const char* normin, const int* n, const double* a, const int* lda, double* x, double* scale, double* cnorm, int* info); extern void dlatzm_(const char* side, const int* m, const int* n, const double* v, const int* incv, const double* tau, double* c1, double* c2, const int* ldc, double* work); extern void dlauu2_(const char* uplo, const int* n, double* a, const int* lda, int* info); extern void dlauum_(const char* uplo, const int* n, double* a, const int* lda, int* info); extern int izmax1_(const int *n, Rcomplex *cx, const int *incx); extern void zgecon_(const char *norm, const int *n, const Rcomplex *a, const int *lda, const double *anorm, double *rcond, Rcomplex *work, double *rwork, int *info); extern void zgesv_(const int *n, const int *nrhs, Rcomplex *a, const int *lda, int *ipiv, Rcomplex *b, const int *ldb, int *info); extern void zgeqp3_(const int *m, const int *n, Rcomplex *a, const int *lda, int *jpvt, Rcomplex *tau, Rcomplex *work, const int *lwork, double *rwork, int *info); extern void zunmqr_(const char *side, const char *trans, const int *m, const int *n, const int *k, Rcomplex *a, const int *lda, Rcomplex *tau, Rcomplex *c, const int *ldc, Rcomplex *work, const int *lwork, int *info); extern void ztrtrs_(const char *uplo, const char *trans, const char *diag, const int *n, const int *nrhs, Rcomplex *a, const int *lda, Rcomplex *b, const int *ldb, int *info); extern void zgesvd_(const char *jobu, const char *jobvt, const int *m, const int *n, Rcomplex *a, const int *lda, double *s, Rcomplex *u, const int *ldu, Rcomplex *vt, const int *ldvt, Rcomplex *work, const int *lwork, double *rwork, int *info); extern void zheev_(const char *jobz, const char *uplo, const int *n, Rcomplex *a, const int *lda, double *w, Rcomplex *work, const int *lwork, double *rwork, int *info); extern void zgeev_(const char *jobvl, const char *jobvr, const int *n, Rcomplex *a, const int *lda, Rcomplex *wr, Rcomplex *vl, const int *ldvl, Rcomplex *vr, const int *ldvr, Rcomplex *work, const int *lwork, double *rwork, int *info); extern double dzsum1_(const int *n, Rcomplex *CX, const int *incx); extern void zlacn2_(const int *n, Rcomplex *v, Rcomplex *x, double *est, int *kase, int *isave); extern double zlantr_(const char *norm, const char *uplo, const char *diag, const int *m, const int *n, Rcomplex *a, const int *lda, double *work); extern void dbdsdc_(char *uplo, char *compq, int *n, double * d, double *e, double *u, int *ldu, double *vt, int *ldvt, double *q, int *iq, double *work, int * iwork, int *info); extern void dgegs_(char *jobvsl, char *jobvsr, int *n, double *a, int *lda, double *b, int *ldb, double * alphar, double *alphai, double *Rf_beta, double *vsl, int *ldvsl, double *vsr, int *ldvsr, double *work, int *lwork, int *info); extern void dgelsd_(int *m, int *n, int *nrhs, double *a, int *lda, double *b, int *ldb, double * s, double *rcond, int *rank, double *work, int *lwork, int *iwork, int *info); extern void dgelsx_(int *m, int *n, int *nrhs, double *a, int *lda, double *b, int *ldb, int * jpvt, double *rcond, int *rank, double *work, int * info); extern void dgesc2_(int *n, double *a, int *lda, double *rhs, int *ipiv, int *jpiv, double *scale); extern void dgesdd_(const char *jobz, const int *m, const int *n, double *a, const int *lda, double *s, double *u, const int *ldu, double *vt, const int *ldvt, double *work, const int *lwork, int *iwork, int *info); extern void dgetc2_(int *n, double *a, int *lda, int *ipiv, int *jpiv, int *info); typedef int (*L_fp)(); extern void dggesx_(char *jobvsl, char *jobvsr, char *sort, L_fp delctg, char *sense, int *n, double *a, int *lda, double *b, int *ldb, int *sdim, double *alphar, double *alphai, double *Rf_beta, double *vsl, int *ldvsl, double *vsr, int *ldvsr, double *rconde, double * rcondv, double *work, int *lwork, int *iwork, int * liwork, int *bwork, int *info); extern void dggev_(char *jobvl, char *jobvr, int *n, double * a, int *lda, double *b, int *ldb, double *alphar, double *alphai, double *Rf_beta, double *vl, int *ldvl, double *vr, int *ldvr, double *work, int *lwork, int *info); extern void dggevx_(char *balanc, char *jobvl, char *jobvr, char * sense, int *n, double *a, int *lda, double *b, int *ldb, double *alphar, double *alphai, double * Rf_beta, double *vl, int *ldvl, double *vr, int *ldvr, int *ilo, int *ihi, double *lscale, double *rscale, double *abnrm, double *bbnrm, double *rconde, double * rcondv, double *work, int *lwork, int *iwork, int * bwork, int *info); extern void dggsvp_(char *jobu, char *jobv, char *jobq, int *m, int *p, int *n, double *a, int *lda, double *b, int *ldb, double *tola, double *tolb, int *k, int *l, double *u, int *ldu, double *v, int *ldv, double *q, int *ldq, int *iwork, double *tau, double *work, int *info); extern void dgtts2_(int *itrans, int *n, int *nrhs, double *dl, double *d, double *du, double *du2, int *ipiv, double *b, int *ldb); extern void dlagv2_(double *a, int *lda, double *b, int *ldb, double *alphar, double *alphai, double * Rf_beta, double *csl, double *snl, double *csr, double * snr); extern void dlals0_(int *icompq, int *nl, int *nr, int *sqre, int *nrhs, double *b, int *ldb, double *bx, int *ldbx, int *perm, int *givptr, int *givcol, int *ldgcol, double *givnum, int *ldgnum, double * poles, double *difl, double *difr, double *z, int * k, double *c, double *s, double *work, int *info); extern void dlalsa_(int *icompq, int *smlsiz, int *n, int *nrhs, double *b, int *ldb, double *bx, int * ldbx, double *u, int *ldu, double *vt, int *k, double *difl, double *difr, double *z, double * poles, int *givptr, int *givcol, int *ldgcol, int * perm, double *givnum, double *c, double *s, double * work, int *iwork, int *info); extern void dlalsd_(char *uplo, int *smlsiz, int *n, int *nrhs, double *d, double *e, double *b, int *ldb, double *rcond, int *rank, double *work, int *iwork, int *info); extern void dlamc1_(int *Rf_beta, int *t, int *rnd, int *ieee1); extern void dlamc2_(int *Rf_beta, int *t, int *rnd, double *eps, int *emin, double *rmin, int *emax, double *rmax); extern double dlamc3_(double *a, double *b); extern void dlamc4_(int *emin, double *start, int *base); extern void dlamc5_(int *Rf_beta, int *p, int *emin, int *ieee, int *emax, double *rmax); extern void dlaqp2_(int *m, int *n, int *offset, double *a, int *lda, int *jpvt, double *tau, double *vn1, double *vn2, double *work); extern void dlaqps_(int *m, int *n, int *offset, int *nb, int *kb, double *a, int *lda, int *jpvt, double *tau, double *vn1, double *vn2, double *auxv, double *f, int *ldf); extern void dlar1v_(int *n, int *b1, int *bn, double *sigma, double *d, double *l, double *ld, double * lld, double *gersch, double *z, double *ztz, double *mingma, int *r, int *isuppz, double *work); extern void dlarrb_(int *n, double *d, double *l, double *ld, double *lld, int *ifirst, int *ilast, double *sigma, double *reltol, double *w, double * wgap, double *werr, double *work, int *iwork, int * info); extern void dlarre_(int *n, double *d, double *e, double *tol, int *nsplit, int *isplit, int *m, double *w, double *woff, double *gersch, double *work, int *info); extern void dlarrf_(int *n, double *d, double *l, double *ld, double *lld, int *ifirst, int *ilast, double *w, double *dplus, double *lplus, double *work, int *iwork, int *info); extern void dlarrv_(int *n, double *d, double *l, int *isplit, int *m, double *w, int *iblock, double *gersch, double *tol, double *z, int *ldz, int *isuppz, double *work, int *iwork, int *info); extern void dlarz_(char *side, int *m, int *n, int *l, double *v, int *incv, double *tau, double *c, int *ldc, double *work); extern void dlarzb_(char *side, char *trans, char *direct, char * storev, int *m, int *n, int *k, int *l, double *v, int *ldv, double *t, int *ldt, double *c, int * ldc, double *work, int *ldwork); extern void dlarzt_(char *direct, char *storev, int *n, int * k, double *v, int *ldv, double *tau, double *t, int *ldt); extern void dlasd0_(int *n, int *sqre, double *d, double *e, double *u, int *ldu, double *vt, int * ldvt, int *smlsiz, int *iwork, double *work, int * info); extern void dlasd1_(int *nl, int *nr, int *sqre, double *d, double *alpha, double *Rf_beta, double *u, int *ldu, double *vt, int *ldvt, int *idxq, int * iwork, double *work, int *info); extern void dlasd2_(int *nl, int *nr, int *sqre, int *k, double *d, double *z, double *alpha, double * Rf_beta, double *u, int *ldu, double *vt, int *ldvt, double *dsigma, double *u2, int *ldu2, double *vt2, int *ldvt2, int *idxp, int *idx, int *idxc, int * idxq, int *coltyp, int *info); extern void dlasd3_(int *nl, int *nr, int *sqre, int *k, double *d, double *q, int *ldq, double *dsigma, double *u, int *ldu, double *u2, int *ldu2, double *vt, int *ldvt, double *vt2, int *ldvt2, int *idxc, int *ctot, double *z, int *info); extern void dlasd4_(int *n, int *i, double *d, double *z, double *delta, double *rho, double * sigma, double *work, int *info); extern void dlasd5_(int *i, double *d, double *z, double *delta, double *rho, double *dsigma, double * work); extern void dlasd6_(int *icompq, int *nl, int *nr, int *sqre, double *d, double *vf, double *vl, double *alpha, double *Rf_beta, int *idxq, int *perm, int *givptr, int *givcol, int *ldgcol, double *givnum, int *ldgnum, double *poles, double *difl, double * difr, double *z, int *k, double *c, double *s, double *work, int *iwork, int *info); extern void dlasd7_(int *icompq, int *nl, int *nr, int *sqre, int *k, double *d, double *z, double *zw, double *vf, double *vfw, double *vl, double *vlw, double *alpha, double *Rf_beta, double * dsigma, int *idx, int *idxp, int *idxq, int *perm, int *givptr, int *givcol, int *ldgcol, double *givnum, int *ldgnum, double *c, double *s, int *info); extern void dlasd8_(int *icompq, int *k, double *d, double *z, double *vf, double *vl, double *difl, double *difr, int *lddifr, double *dsigma, double * work, int *info); extern void dlasd9_(int *icompq, int *ldu, int *k, double *d, double *z, double *vf, double *vl, double *difl, double *difr, double *dsigma, double * work, int *info); extern void dlasda_(int *icompq, int *smlsiz, int *n, int *sqre, double *d, double *e, double *u, int *ldu, double *vt, int *k, double *difl, double *difr, double *z, double *poles, int *givptr, int *givcol, int *ldgcol, int *perm, double *givnum, double *c, double *s, double *work, int *iwork, int *info); extern void dlasdq_(char *uplo, int *sqre, int *n, int * ncvt, int *nru, int *ncc, double *d, double *e, double *vt, int *ldvt, double *u, int *ldu, double *c, int *ldc, double *work, int *info); extern void dlasdt_(int *n, int *lvl, int *nd, int * inode, int *ndiml, int *ndimr, int *msub); extern void dlasq5_(int *i0, int *n0, double *z, int *pp, double *tau, double *dmin, double *dmin1, double *dmin2, double *dn, double *dnm1, double *dnm2, int *ieee); extern void dlasq6_(int *i0, int *n0, double *z, int *pp, double *dmin, double *dmin1, double *dmin2, double *dn, double *dnm1, double *dnm2); extern void dlatdf_(int *ijob, int *n, double *z, int *ldz, double *rhs, double *rdsum, double *rdscal, int *ipiv, int *jpiv); extern void dlatrz_(int *m, int *n, int *l, double * a, int *lda, double *tau, double *work); extern void dormr3_(char *side, char *trans, int *m, int *n, int *k, int *l, double *a, int *lda, double *tau, double *c, int *ldc, double *work, int *info); extern void dormrz_(char *side, char *trans, int *m, int *n, int *k, int *l, double *a, int *lda, double *tau, double *c, int *ldc, double *work, int *lwork, int *info); extern void dptts2_(int *n, int *nrhs, double *d, double *e, double *b, int *ldb); extern void dsbgvd_(char *jobz, char *uplo, int *n, int *ka, int *kb, double *ab, int *ldab, double *bb, int * ldbb, double *w, double *z, int *ldz, double *work, int *lwork, int *iwork, int *liwork, int *info); extern void dsbgvx_(char *jobz, char *range, char *uplo, int *n, int *ka, int *kb, double *ab, int *ldab, double * bb, int *ldbb, double *q, int *ldq, double *vl, double *vu, int *il, int *iu, double *abstol, int *m, double *w, double *z, int *ldz, double *work, int *iwork, int *ifail, int *info); extern void dspgvd_(int *itype, char *jobz, char *uplo, int * n, double *ap, double *bp, double *w, double *z, int *ldz, double *work, int *lwork, int *iwork, int *liwork, int *info); extern void dspgvx_(int *itype, char *jobz, char *range, char * uplo, int *n, double *ap, double *bp, double *vl, double *vu, int *il, int *iu, double *abstol, int *m, double *w, double *z, int *ldz, double *work, int *iwork, int *ifail, int *info); extern void dstegr_(char *jobz, char *range, int *n, double * d, double *e, double *vl, double *vu, int *il, int *iu, double *abstol, int *m, double *w, double *z, int *ldz, int *isuppz, double *work, int *lwork, int *iwork, int *liwork, int *info); extern void dstevr_(char *jobz, char *range, int *n, double * d, double *e, double *vl, double *vu, int *il, int *iu, double *abstol, int *m, double *w, double *z, int *ldz, int *isuppz, double *work, int *lwork, int *iwork, int *liwork, int *info); extern void dsygvd_(int *itype, char *jobz, char *uplo, int * n, double *a, int *lda, double *b, int *ldb, double *w, double *work, int *lwork, int *iwork, int *liwork, int *info); extern void dsygvx_(int *itype, char *jobz, char *range, char * uplo, int *n, double *a, int *lda, double *b, int *ldb, double *vl, double *vu, int *il, int *iu, double *abstol, int *m, double *w, double *z, int *ldz, double *work, int *lwork, int *iwork, int *ifail, int *info); extern void dtgex2_(int *wantq, int *wantz, int *n, double *a, int *lda, double *b, int *ldb, double * q, int *ldq, double *z, int *ldz, int *j1, int * n1, int *n2, double *work, int *lwork, int *info); extern void dtgexc_(int *wantq, int *wantz, int *n, double *a, int *lda, double *b, int *ldb, double * q, int *ldq, double *z, int *ldz, int *ifst, int *ilst, double *work, int *lwork, int *info); extern void dtgsen_(int *ijob, int *wantq, int *wantz, int *select, int *n, double *a, int *lda, double * b, int *ldb, double *alphar, double *alphai, double * Rf_beta, double *q, int *ldq, double *z, int *ldz, int *m, double *pl, double *pr, double *dif, double *work, int *lwork, int *iwork, int *liwork, int *info); extern void dtgsna_(char *job, char *howmny, int *select, int *n, double *a, int *lda, double *b, int *ldb, double *vl, int *ldvl, double *vr, int *ldvr, double *s, double *dif, int *mm, int *m, double * work, int *lwork, int *iwork, int *info); extern void dtgsy2_(char *trans, int *ijob, int *m, int * n, double *a, int *lda, double *b, int *ldb, double *c, int *ldc, double *d, int *ldd, double *e, int *lde, double *f, int *ldf, double * scale, double *rdsum, double *rdscal, int *iwork, int *pq, int *info); extern void dtgsyl_(char *trans, int *ijob, int *m, int * n, double *a, int *lda, double *b, int *ldb, double *c, int *ldc, double *d, int *ldd, double *e, int *lde, double *f, int *ldf, double * scale, double *dif, double *work, int *lwork, int * iwork, int *info); extern void dtzrzf_(int *m, int *n, double *a, int * lda, double *tau, double *work, int *lwork, int *info); extern void dpstrf_(const char* uplo, const int* n, double* a, const int* lda, int* piv, int* rank, double* tol, double *work, int* info); extern int lsame_(char *ca, char *cb); extern void zbdsqr_(char *uplo, int *n, int *ncvt, int * nru, int *ncc, double *d, double *e, Rcomplex *vt, int *ldvt, Rcomplex *u, int *ldu, Rcomplex *c, int *ldc, double *rwork, int *info); extern void zdrot_(int *n, Rcomplex *cx, int *incx, Rcomplex *cy, int *incy, double *c, double *s); extern void zgebak_(char *job, char *side, int *n, int *ilo, int *ihi, double *scale, int *m, Rcomplex *v, int *ldv, int *info); extern void zgebal_(char *job, int *n, Rcomplex *a, int *lda, int *ilo, int *ihi, double *scale, int *info); extern void zgebd2_(int *m, int *n, Rcomplex *a, int *lda, double *d, double *e, Rcomplex *tauq, Rcomplex *taup, Rcomplex *work, int *info); extern void zgebrd_(int *m, int *n, Rcomplex *a, int *lda, double *d, double *e, Rcomplex *tauq, Rcomplex *taup, Rcomplex *work, int *lwork, int * info); extern void zgehd2_(int *n, int *ilo, int *ihi, Rcomplex *a, int *lda, Rcomplex *tau, Rcomplex * work, int *info); extern void zgehrd_(int *n, int *ilo, int *ihi, Rcomplex *a, int *lda, Rcomplex *tau, Rcomplex * work, int *lwork, int *info); extern void zgelq2_(int *m, int *n, Rcomplex *a, int *lda, Rcomplex *tau, Rcomplex *work, int *info); extern void zgelqf_(int *m, int *n, Rcomplex *a, int *lda, Rcomplex *tau, Rcomplex *work, int *lwork, int *info); extern void zgeqr2_(int *m, int *n, Rcomplex *a, int *lda, Rcomplex *tau, Rcomplex *work, int *info); extern void zgeqrf_(int *m, int *n, Rcomplex *a, int *lda, Rcomplex *tau, Rcomplex *work, int *lwork, int *info); extern void zgetf2_(int *m, int *n, Rcomplex *a, int *lda, int *ipiv, int *info); extern void zgetrf_(int *m, int *n, Rcomplex *a, int *lda, int *ipiv, int *info); extern void zgetrs_(char *trans, int *n, int *nrhs, Rcomplex *a, int *lda, int *ipiv, Rcomplex *b, int *ldb, int *info); extern void zhetd2_(char *uplo, int *n, Rcomplex *a, int *lda, double *d, double *e, Rcomplex *tau, int *info); extern void zhetrd_(char *uplo, int *n, Rcomplex *a, int *lda, double *d, double *e, Rcomplex *tau, Rcomplex *work, int *lwork, int *info); extern void zhseqr_(char *job, char *compz, int *n, int *ilo, int *ihi, Rcomplex *h, int *ldh, Rcomplex *w, Rcomplex *z, int *ldz, Rcomplex *work, int *lwork, int *info); extern void zlabrd_(int *m, int *n, int *nb, Rcomplex *a, int *lda, double *d, double *e, Rcomplex *tauq, Rcomplex *taup, Rcomplex *x, int * ldx, Rcomplex *y, int *ldy); extern void zlacgv_(int *n, Rcomplex *x, int *incx); extern void zlacpy_(char *uplo, int *m, int *n, Rcomplex *a, int *lda, Rcomplex *b, int *ldb); extern void zlahqr_(int *wantt, int *wantz, int *n, int *ilo, int *ihi, Rcomplex *h, int *ldh, Rcomplex *w, int *iloz, int *ihiz, Rcomplex *z, int *ldz, int *info); extern void zlahrd_(int *n, int *k, int *nb, Rcomplex *a, int *lda, Rcomplex *tau, Rcomplex *t, int *ldt, Rcomplex *y, int *ldy); extern double zlange_(char *norm, int *m, int *n, Rcomplex *a, int *lda, double *work); extern double zlanhe_(char *norm, char *uplo, int *n, Rcomplex *a, int *lda, double *work); extern double zlanhs_(char *norm, int *n, Rcomplex *a, int *lda, double *work); extern void zlaqp2_(int *m, int *n, int *offset, Rcomplex *a, int *lda, int *jpvt, Rcomplex *tau, double *vn1, double *vn2, Rcomplex *work); extern void zlaqps_(int *m, int *n, int *offset, int *nb, int *kb, Rcomplex *a, int *lda, int *jpvt, Rcomplex *tau, double *vn1, double *vn2, Rcomplex * auxv, Rcomplex *f, int *ldf); extern void zlarf_(char *side, int *m, int *n, Rcomplex *v, int *incv, Rcomplex *tau, Rcomplex *c, int * ldc, Rcomplex *work); extern void zlarfb_(char *side, char *trans, char *direct, char * storev, int *m, int *n, int *k, Rcomplex *v, int *ldv, Rcomplex *t, int *ldt, Rcomplex *c, int * ldc, Rcomplex *work, int *ldwork); extern void zlarfg_(int *n, Rcomplex *alpha, Rcomplex * x, int *incx, Rcomplex *tau); extern void zlarft_(char *direct, char *storev, int *n, int * k, Rcomplex *v, int *ldv, Rcomplex *tau, Rcomplex * t, int *ldt); extern void zlarfx_(char *side, int *m, int *n, Rcomplex *v, Rcomplex *tau, Rcomplex *c, int * ldc, Rcomplex *work); extern void zlascl_(char *type, int *kl, int *ku, double *cfrom, double *cto, int *m, int *n, Rcomplex *a, int *lda, int *info); extern void zlaset_(char *uplo, int *m, int *n, Rcomplex *alpha, Rcomplex *Rf_beta, Rcomplex *a, int * lda); extern void zlasr_(char *side, char *pivot, char *direct, int *m, int *n, double *c, double *s, Rcomplex *a, int *lda); extern void zlassq_(int *n, Rcomplex *x, int *incx, double *scale, double *sumsq); extern void zlaswp_(int *n, Rcomplex *a, int *lda, int *k1, int *k2, int *ipiv, int *incx); extern void zlatrd_(char *uplo, int *n, int *nb, Rcomplex *a, int *lda, double *e, Rcomplex *tau, Rcomplex *w, int *ldw); extern void zlatrs_(char *uplo, char *trans, char *diag, char * normin, int *n, Rcomplex *a, int *lda, Rcomplex *x, double *scale, double *cnorm, int *info); extern void zsteqr_(char *compz, int *n, double *d, double *e, Rcomplex *z, int *ldz, double *work, int *info); extern void ztrcon_(const char *norm, const char *uplo, const char *diag, const int *n, const Rcomplex *a, const int *lda, double *rcond, Rcomplex *work, double *rwork, int *info); extern void ztrevc_(char *side, char *howmny, int *select, int *n, Rcomplex *t, int *ldt, Rcomplex *vl, int *ldvl, Rcomplex *vr, int *ldvr, int *mm, int *m, Rcomplex *work, double *rwork, int *info); extern void zung2l_(int *m, int *n, int *k, Rcomplex *a, int *lda, Rcomplex *tau, Rcomplex * work, int *info); extern void zung2r_(int *m, int *n, int *k, Rcomplex *a, int *lda, Rcomplex *tau, Rcomplex * work, int *info); extern void zungbr_(char *vect, int *m, int *n, int *k, Rcomplex *a, int *lda, Rcomplex *tau, Rcomplex * work, int *lwork, int *info); extern void zunghr_(int *n, int *ilo, int *ihi, Rcomplex *a, int *lda, Rcomplex *tau, Rcomplex * work, int *lwork, int *info); extern void zungl2_(int *m, int *n, int *k, Rcomplex *a, int *lda, Rcomplex *tau, Rcomplex * work, int *info); extern void zunglq_(int *m, int *n, int *k, Rcomplex *a, int *lda, Rcomplex *tau, Rcomplex * work, int *lwork, int *info); extern void zungql_(int *m, int *n, int *k, Rcomplex *a, int *lda, Rcomplex *tau, Rcomplex * work, int *lwork, int *info); extern void zungqr_(int *m, int *n, int *k, Rcomplex *a, int *lda, Rcomplex *tau, Rcomplex * work, int *lwork, int *info); extern void zungr2_(int *m, int *n, int *k, Rcomplex *a, int *lda, Rcomplex *tau, Rcomplex * work, int *info); extern void zungrq_(int *m, int *n, int *k, Rcomplex *a, int *lda, Rcomplex *tau, Rcomplex * work, int *lwork, int *info); extern void zungtr_(char *uplo, int *n, Rcomplex *a, int *lda, Rcomplex *tau, Rcomplex *work, int *lwork, int *info); extern void zunm2r_(char *side, char *trans, int *m, int *n, int *k, Rcomplex *a, int *lda, Rcomplex *tau, Rcomplex *c, int *ldc, Rcomplex *work, int *info); extern void zunmbr_(char *vect, char *side, char *trans, int *m, int *n, int *k, Rcomplex *a, int *lda, Rcomplex *tau, Rcomplex *c, int *ldc, Rcomplex *work, int * lwork, int *info); extern void zunml2_(char *side, char *trans, int *m, int *n, int *k, Rcomplex *a, int *lda, Rcomplex *tau, Rcomplex *c, int *ldc, Rcomplex *work, int *info); extern void zunmlq_(char *side, char *trans, int *m, int *n, int *k, Rcomplex *a, int *lda, Rcomplex *tau, Rcomplex *c, int *ldc, Rcomplex *work, int *lwork, int *info); extern void zgesdd_(const char *jobz, const int *m, const int *n, Rcomplex *a, const int *lda, double *s, Rcomplex *u, const int *ldu, Rcomplex *vt, const int *ldvt, Rcomplex *work, const int *lwork, double *rwork, int *iwork, int *info); extern void zgelsd_(int *m, int *n, int *nrhs, Rcomplex *a, int *lda, Rcomplex *b, int *ldb, double *s, double *rcond, int *rank, Rcomplex *work, int *lwork, double *rwork, int *iwork, int *info); } # /Users/ls/Source/git/fastr/com.oracle.truffle.r.native/include/R_ext/Linpack.h extern "C" { extern void dpbfa_(double*, int*, int*, int*, int*); // dpbfa_ unused extern void dpbsl_(double*, int*, int*, int*, double*); // dpbsl_ unused extern void dpoco_(double*, int*, int*, double*, double*, int*); // dpoco_ used 4 times in locpol extern void dpodi_(double*, int*, int*, double*, int*); // dpodi_ used 3 times in locpol extern void dpofa_(double*, int*, int*, int*); // dpofa_ unused extern void dposl_(double*, int*, int*, double*); // dposl_ used 4 times in locpol extern void dqrdc_(double*, int*, int*, int*, double*, int*, double*, int*); // dqrdc_ unused extern void dqrsl_(double*, int*, int*, int*, double*, double*, double*, double*, double*, double*, double*, int*, int*); // dqrsl_ used 3 times in earth extern void dsvdc_(double*, int*, int*, int*, double*, double*, double*, int*, double*, int*, double*, int*, int*); // dsvdc_ unused extern void dtrco_(double*, int*, int*, double*, double*, int*); // dtrco_ unused extern void dtrsl_(double*, int*, int*, double*, int*, int*); // dtrsl_ used 2 times in earth extern void dchdc_(double*, int*, int*, double*, int*, int*, int*); // dchdc_ unused extern void dchdd_(double*, int*, int*, double*, double*, int*, int*, double*, double*, double*, double*, int*); // dchdd_ unused extern void dchex_(double*, int*, int*, int*, int*, double*, int*, int*, double*, double*, int*); // dchex_ unused extern void dchud_(double*, int*, int*, double*, double*, int*, int*, double*, double*, double*, double*); // dchud_ unused extern void dgbco_(double*, int*, int*, int*, int*, int*, double*, double*); // dgbco_ unused extern void dgbdi_(double*, int*, int*, int*, int*, int*, double*); // dgbdi_ unused extern void dgbfa_(double*, int*, int*, int*, int*, int*, int*); // dgbfa_ unused extern void dgbsl_(double*, int*, int*, int*, int*, int*, double*, int*); // dgbsl_ unused extern void dgeco_(double*, int*, int*, int*, double*, double*); // dgeco_ unused extern void dgedi_(double*, int*, int*, int*, double*, double*, int*); // dgedi_ unused extern void dgefa_(double*, int*, int*, int*, int*); // dgefa_ unused extern void dgesl_(double*, int*, int*, int*, double*, int*); // dgesl_ unused extern void dgtsl_(int*, double*, double*, double*, double*, int*); // dgtsl_ unused extern void dpbco_(double*, int*, int*, int*, double*, double*, int*); // dpbco_ unused extern void dpbdi_(double*, int*, int*, int*, double*); // dpbdi_ unused extern void dppco_(double*, int*, double*, double*, int*); // dppco_ unused extern void dppdi_(double*, int*, double*, int*); // dppdi_ unused extern void dppfa_(double*, int*, int*); // dppfa_ unused extern void dppsl_(double*, int*, double*); // dppsl_ unused extern void dptsl_(int*, double*, double*, double*); // dptsl_ unused extern void dsico_(double*, int*, int*, int*, double*, double*); // dsico_ unused extern void dsidi_(double*, int*, int*, int*, double*, int*, double*, int*); // dsidi_ unused extern void dsifa_(double*, int*, int*, int*, int*); // dsifa_ unused extern void dsisl_(double*, int*, int*, int*, double*); // dsisl_ unused extern void dspco_(double*, int*, int*, double*, double*); // dspco_ unused extern void dspdi_(double*, int*, int*, double*, int*, double*, int*); // dspdi_ unused extern void dspfa_(double*, int*, int*, int*); // dspfa_ unused extern void dspsl_(double*, int*, int*, double*); // dspsl_ unused } # /Users/ls/Source/git/fastr/com.oracle.truffle.r.native/include/R_ext/MathThreads.h extern "C" { extern int R_num_math_threads; // R_num_math_threads used 2 times in apcluster extern int R_max_num_math_threads; // R_max_num_math_threads unused } # /Users/ls/Source/git/fastr/com.oracle.truffle.r.native/include/R_ext/Memory.h extern "C" { void* vmaxget(void); // vmaxget used 279 times in 20 packages void vmaxset(const void *); // vmaxset used 279 times in 20 packages void R_gc(void); // R_gc used 6 times in TMB, excel.link, gmatrix, microbenchmark int R_gc_running(); // R_gc_running unused char* R_alloc(size_t, int); // R_alloc used 7787 times in 330 packages long double *R_allocLD(size_t nelem); char* S_alloc(long, int); // S_alloc used 540 times in 50 packages char* S_realloc(char *, long, long, int); // S_realloc used 55 times in 11 packages } # /Users/ls/Source/git/fastr/com.oracle.truffle.r.native/include/R_ext/Parse.h extern "C" { typedef enum { PARSE_NULL, PARSE_OK, PARSE_INCOMPLETE, PARSE_ERROR, PARSE_EOF } ParseStatus; // ParseStatus used 25 times in 11 packages SEXP R_ParseVector(SEXP, int, ParseStatus *, SEXP); // R_ParseVector used 21 times in 11 packages } # /Users/ls/Source/git/fastr/com.oracle.truffle.r.native/include/R_ext/Print.h extern "C" { void Rprintf(const char *, ...); // Rprintf used 33813 times in 729 packages void REprintf(const char *, ...); // REprintf used 2531 times in 135 packages } # /Users/ls/Source/git/fastr/com.oracle.truffle.r.native/include/R_ext/PrtUtil.h extern "C" { void Rf_formatLogical(int *, R_xlen_t, int *); // Rf_formatLogical unused // formatLogical used 2 times in qtbase, RGtk2 void Rf_formatInteger(int *, R_xlen_t, int *); // Rf_formatInteger unused // formatInteger used 2 times in qtbase, RGtk2 void Rf_formatReal(double *, R_xlen_t, int *, int *, int *, int); // Rf_formatReal used 2 times in Rcpp, Rcpp11 // formatReal used 5 times in data.table, qtbase, RGtk2 void Rf_formatComplex(Rcomplex *, R_xlen_t, int *, int *, int *, int *, int *, int *, int); // Rf_formatComplex used 2 times in Rcpp, Rcpp11 // formatComplex unused const char *Rf_EncodeLogical(int, int); // Rf_EncodeLogical unused // EncodeLogical used 2 times in qtbase, RGtk2 const char *Rf_EncodeInteger(int, int); // Rf_EncodeInteger unused // EncodeInteger used 2 times in qtbase, RGtk2 const char *Rf_EncodeReal0(double, int, int, int, const char *); // Rf_EncodeReal0 unused // EncodeReal0 unused const char *Rf_EncodeComplex(Rcomplex, int, int, int, int, int, int, const char *); // Rf_EncodeComplex used 2 times in Rcpp, Rcpp11 // EncodeComplex unused const char *Rf_EncodeReal(double, int, int, int, char); // Rf_EncodeReal used 2 times in Rcpp, Rcpp11 // EncodeReal used 2 times in qtbase, RGtk2 int Rf_IndexWidth(R_xlen_t); // Rf_IndexWidth unused // IndexWidth unused void Rf_VectorIndex(R_xlen_t, int); // Rf_VectorIndex unused // VectorIndex used 6 times in gnmf void Rf_printIntegerVector(int *, R_xlen_t, int); // Rf_printIntegerVector unused // printIntegerVector used 2 times in bvpSolve, deTestSet void Rf_printRealVector (double *, R_xlen_t, int); // Rf_printRealVector unused // printRealVector used 2 times in bvpSolve, deTestSet void Rf_printComplexVector(Rcomplex *, R_xlen_t, int); // Rf_printComplexVector unused // printComplexVector unused } # /Users/ls/Source/git/fastr/com.oracle.truffle.r.native/include/R_ext/QuartzDevice.h extern "C" { typedef void* QuartzDesc_t; typedef struct QuartzBackend_s { int size; double width, height; double scalex, scaley, pointsize; int bg, canvas; int flags; void* userInfo; CGContextRef (*getCGContext)(QuartzDesc_t dev, void*userInfo); int (*locatePoint)(QuartzDesc_t dev, void*userInfo, double*x, double*y); void (*close)(QuartzDesc_t dev, void*userInfo); void (*newPage)(QuartzDesc_t dev, void*userInfo, int flags); void (*state)(QuartzDesc_t dev, void*userInfo, int state); void* (*par)(QuartzDesc_t dev, void*userInfo, int set, const char *key, void *value); void (*sync)(QuartzDesc_t dev, void*userInfo); void* (*cap)(QuartzDesc_t dev, void*userInfo); } QuartzBackend_t; // QuartzBackend_t unused typedef struct QuartzParameters_s { int size; const char *type, *file, *title; double x, y, width, height, pointsize; const char *family; int flags; int connection; int bg, canvas; double *dpi; double pard1, pard2; int pari1, pari2; const char *pars1, *pars2; void *parv; } QuartzParameters_t; // QuartzParameters_t unused QuartzDesc_t QuartzDevice_Create(void *dd, QuartzBackend_t* def); // QuartzDevice_Create unused typedef struct QuartzFunctons_s { void* (*Create)(void *, QuartzBackend_t *); int (*DevNumber)(QuartzDesc_t desc); void (*Kill)(QuartzDesc_t desc); void (*ResetContext)(QuartzDesc_t desc); double (*GetWidth)(QuartzDesc_t desc); double (*GetHeight)(QuartzDesc_t desc); void (*SetSize)(QuartzDesc_t desc, double width, double height); double (*GetScaledWidth)(QuartzDesc_t desc); double (*GetScaledHeight)(QuartzDesc_t desc); void (*SetScaledSize)(QuartzDesc_t desc, double width, double height); double (*GetXScale)(QuartzDesc_t desc); double (*GetYScale)(QuartzDesc_t desc); void (*SetScale)(QuartzDesc_t desc,double scalex, double scaley); void (*SetTextScale)(QuartzDesc_t desc,double scale); double (*GetTextScale)(QuartzDesc_t desc); void (*SetPointSize)(QuartzDesc_t desc,double ps); double (*GetPointSize)(QuartzDesc_t desc); int (*GetDirty)(QuartzDesc_t desc); void (*SetDirty)(QuartzDesc_t desc,int dirty); void (*ReplayDisplayList)(QuartzDesc_t desc); void* (*GetSnapshot)(QuartzDesc_t desc, int last); void (*RestoreSnapshot)(QuartzDesc_t desc,void* snapshot); int (*GetAntialias)(QuartzDesc_t desc); void (*SetAntialias)(QuartzDesc_t desc, int aa); int (*GetBackground)(QuartzDesc_t desc); void (*Activate)(QuartzDesc_t desc); void* (*SetParameter)(QuartzDesc_t desc, const char *key, void *value); void* (*GetParameter)(QuartzDesc_t desc, const char *key); } QuartzFunctions_t; // QuartzFunctions_t unused QuartzFunctions_t *getQuartzFunctions(); // getQuartzFunctions unused typedef QuartzDesc_t (*quartz_create_fn_t)(void *dd, QuartzFunctions_t *fn, QuartzParameters_t *par); extern QuartzDesc_t (*ptr_QuartzBackend)(void *dd, QuartzFunctions_t *fn, QuartzParameters_t *par); QuartzDesc_t Quartz_C(QuartzParameters_t *par, quartz_create_fn_t q_create, int *errorCode); // Quartz_C unused } # /Users/ls/Source/git/fastr/com.oracle.truffle.r.native/include/R_ext/R-ftp-http.h typedef int_fast64_t DLsize_t; // DLsize_t unused extern "C" { void *R_HTTPOpen(const char *url); // R_HTTPOpen unused int R_HTTPRead(void *ctx, char *dest, int len); // R_HTTPRead unused void R_HTTPClose(void *ctx); // R_HTTPClose unused void *R_FTPOpen(const char *url); // R_FTPOpen unused int R_FTPRead(void *ctx, char *dest, int len); // R_FTPRead unused void R_FTPClose(void *ctx); // R_FTPClose unused void * RxmlNanoHTTPOpen(const char *URL, char **contentType, const char *headers, int cacheOK); // RxmlNanoHTTPOpen unused int RxmlNanoHTTPRead(void *ctx, void *dest, int len); // RxmlNanoHTTPRead unused void RxmlNanoHTTPClose(void *ctx); // RxmlNanoHTTPClose unused int RxmlNanoHTTPReturnCode(void *ctx); // RxmlNanoHTTPReturnCode unused char * RxmlNanoHTTPStatusMsg(void *ctx); // RxmlNanoHTTPStatusMsg unused DLsize_t RxmlNanoHTTPContentLength(void *ctx); // RxmlNanoHTTPContentLength unused char * RxmlNanoHTTPContentType(void *ctx); // RxmlNanoHTTPContentType unused void RxmlNanoHTTPTimeout(int delay); // RxmlNanoHTTPTimeout unused void * RxmlNanoFTPOpen(const char *URL); // RxmlNanoFTPOpen unused int RxmlNanoFTPRead(void *ctx, void *dest, int len); // RxmlNanoFTPRead unused int RxmlNanoFTPClose(void *ctx); // RxmlNanoFTPClose unused void RxmlNanoFTPTimeout(int delay); // RxmlNanoFTPTimeout unused DLsize_t RxmlNanoFTPContentLength(void *ctx); // RxmlNanoFTPContentLength unused void RxmlMessage(int level, const char *format, ...); // RxmlMessage unused void RxmlNanoFTPCleanup(void); // RxmlNanoFTPCleanup unused void RxmlNanoHTTPCleanup(void); // RxmlNanoHTTPCleanup unused } # /Users/ls/Source/git/fastr/com.oracle.truffle.r.native/include/R_ext/RS.h extern "C" { extern void *R_chk_calloc(size_t, size_t); // R_chk_calloc used 6 times in rpart, XML, itree, ifultools, mgcv extern void *R_chk_realloc(void *, size_t); // R_chk_realloc used 5 times in seqminer, gpuR, ifultools, mgcv extern void R_chk_free(void *); // R_chk_free used 2 times in mgcv void call_R(char*, long, void**, char**, long*, char**, long, char**); // call_R used 2 times in PoweR // call_S used 2 times in locfit } # /Users/ls/Source/git/fastr/com.oracle.truffle.r.native/include/R_ext/RStartup.h extern "C" { typedef enum { SA_NORESTORE, SA_RESTORE, SA_DEFAULT, SA_NOSAVE, SA_SAVE, SA_SAVEASK, SA_SUICIDE } SA_TYPE; // SA_TYPE used 7 times in Rserve, rJava, littler typedef struct { Rboolean R_Quiet; Rboolean R_Slave; Rboolean R_Interactive; Rboolean R_Verbose; Rboolean LoadSiteFile; Rboolean LoadInitFile; Rboolean DebugInitFile; SA_TYPE RestoreAction; SA_TYPE SaveAction; size_t vsize; size_t nsize; size_t max_vsize; size_t max_nsize; size_t ppsize; int NoRenviron; } structRstart; // structRstart used 8 times in RInside, rscproxy, Rserve, rJava, littler typedef structRstart *Rstart; void R_DefParams(Rstart); // R_DefParams used 9 times in RInside, rscproxy, Rserve, rJava, littler void R_SetParams(Rstart); // R_SetParams used 14 times in RInside, rscproxy, Rserve, rJava, littler void R_SetWin32(Rstart); // R_SetWin32 used 2 times in Rserve, rJava void R_SizeFromEnv(Rstart); // R_SizeFromEnv used 3 times in Rserve, rJava void R_common_command_line(int *, char **, Rstart); // R_common_command_line used 3 times in Rserve, rJava void R_set_command_line_arguments(int argc, char **argv); // R_set_command_line_arguments used 4 times in Rserve, rJava, rscproxy void setup_Rmainloop(void); // setup_Rmainloop used 6 times in Rserve, rJava, rscproxy } # /Users/ls/Source/git/fastr/com.oracle.truffle.r.native/include/R_ext/Rallocators.h typedef void *(*custom_alloc_t)(R_allocator_t *allocator, size_t); typedef void (*custom_free_t)(R_allocator_t *allocator, void *); struct R_allocator { custom_alloc_t mem_alloc; custom_free_t mem_free; void *res; void *data; }; # /Users/ls/Source/git/fastr/com.oracle.truffle.r.native/include/R_ext/Random.h extern "C" { typedef enum { WICHMANN_HILL, MARSAGLIA_MULTICARRY, SUPER_DUPER, MERSENNE_TWISTER, KNUTH_TAOCP, USER_UNIF, KNUTH_TAOCP2, LECUYER_CMRG } RNGtype; // RNGtype unused typedef enum { BUGGY_KINDERMAN_RAMAGE, AHRENS_DIETER, BOX_MULLER, USER_NORM, INVERSION, KINDERMAN_RAMAGE } N01type; // N01type unused void GetRNGstate(void); // GetRNGstate used 1753 times in 434 packages void PutRNGstate(void); // PutRNGstate used 1794 times in 427 packages double unif_rand(void); // unif_rand used 2135 times in 327 packages double norm_rand(void); // norm_rand used 408 times in 93 packages double exp_rand(void); // exp_rand used 110 times in 25 packages typedef unsigned int Int32; double * user_unif_rand(void); // user_unif_rand used 10 times in randaes, rstream, rngwell19937, SuppDists, randtoolbox, rlecuyer, Rrdrand void user_unif_init(Int32); // user_unif_init used 5 times in randaes, SuppDists, randtoolbox, rngwell19937 int * user_unif_nseed(void); // user_unif_nseed used 4 times in randaes, SuppDists, rngwell19937 int * user_unif_seedloc(void); // user_unif_seedloc used 4 times in randaes, SuppDists, rngwell19937 double * user_norm_rand(void); // user_norm_rand used 1 times in RcppZiggurat } # /Users/ls/Source/git/fastr/com.oracle.truffle.r.native/include/R_ext/Rdynload.h typedef void * (*DL_FUNC)(); typedef unsigned int R_NativePrimitiveArgType; typedef unsigned int R_NativeObjectArgType; typedef enum {R_ARG_IN, R_ARG_OUT, R_ARG_IN_OUT, R_IRRELEVANT} R_NativeArgStyle; typedef struct { const char *name; DL_FUNC fun; int numArgs; R_NativePrimitiveArgType *types; R_NativeArgStyle *styles; } R_CMethodDef; // R_CMethodDef used 76 times in 73 packages typedef R_CMethodDef R_FortranMethodDef; // R_FortranMethodDef used 21 times in 20 packages typedef struct { const char *name; DL_FUNC fun; int numArgs; } R_CallMethodDef; // R_CallMethodDef used 156 times in 147 packages typedef R_CallMethodDef R_ExternalMethodDef; // R_ExternalMethodDef used 8 times in devEMF, rgl, data.table, foreign, actuar, xts, Matrix, Rcpp typedef struct _DllInfo DllInfo; extern "C" { int R_registerRoutines(DllInfo *info, const R_CMethodDef * const croutines, // R_registerRoutines used 209 times in 196 packages const R_CallMethodDef * const callRoutines, const R_FortranMethodDef * const fortranRoutines, const R_ExternalMethodDef * const externalRoutines); Rboolean R_useDynamicSymbols(DllInfo *info, Rboolean value); // R_useDynamicSymbols used 105 times in 102 packages Rboolean R_forceSymbols(DllInfo *info, Rboolean value); // R_forceSymbols used 14 times in 14 packages DllInfo *R_getDllInfo(const char *name); // R_getDllInfo unused DllInfo *R_getEmbeddingDllInfo(void); // R_getEmbeddingDllInfo used 1 times in Rserve typedef struct Rf_RegisteredNativeSymbol R_RegisteredNativeSymbol; typedef enum {R_ANY_SYM=0, R_C_SYM, R_CALL_SYM, R_FORTRAN_SYM, R_EXTERNAL_SYM} NativeSymbolType; DL_FUNC R_FindSymbol(char const *, char const *, // R_FindSymbol used 149 times in ergm, RTextTools, SamplerCompare, network, CCMnet, hergm R_RegisteredNativeSymbol *symbol); void R_RegisterCCallable(const char *package, const char *name, DL_FUNC fptr); // R_RegisterCCallable used 1077 times in 49 packages DL_FUNC R_GetCCallable(const char *package, const char *name); // R_GetCCallable used 1417 times in 41 packages } # /Users/ls/Source/git/fastr/com.oracle.truffle.r.native/include/R_ext/Riconv.h extern "C" { void * Riconv_open (const char* tocode, const char* fromcode); // Riconv_open used 10 times in devEMF, RCurl, pbdZMQ, ore, Nippon, readr size_t Riconv (void * cd, const char **inbuf, size_t *inbytesleft, // Riconv used 14 times in devEMF, RCurl, pbdZMQ, ore, Nippon, readr char **outbuf, size_t *outbytesleft); int Riconv_close (void * cd); // Riconv_close used 7 times in devEMF, pbdZMQ, ore, Nippon, readr } # /Users/ls/Source/git/fastr/com.oracle.truffle.r.native/include/R_ext/Utils.h extern "C" { void R_isort(int*, int); // R_isort used 45 times in 18 packages void R_rsort(double*, int); // R_rsort used 210 times in 29 packages void R_csort(Rcomplex*, int); // R_csort unused void rsort_with_index(double *, int *, int); // rsort_with_index used 77 times in 40 packages void Rf_revsort(double*, int*, int); // Rf_revsort unused // revsort used 60 times in 20 packages void Rf_iPsort(int*, int, int); // Rf_iPsort unused // iPsort used 3 times in matrixStats, robustbase void Rf_rPsort(double*, int, int); // Rf_rPsort unused // rPsort used 63 times in 15 packages void Rf_cPsort(Rcomplex*, int, int); // Rf_cPsort unused // cPsort unused void R_qsort (double *v, size_t i, size_t j); // R_qsort used 10 times in extWeibQuant, pomp, robustbase, dplR, tclust, pcaPP void R_qsort_I (double *v, int *II, int i, int j); // R_qsort_I used 33 times in 15 packages void R_qsort_int (int *iv, size_t i, size_t j); // R_qsort_int unused void R_qsort_int_I(int *iv, int *II, int i, int j); // R_qsort_int_I used 19 times in ff, matrixStats, arules, Rborist, slam, eco, bnlearn const char *R_ExpandFileName(const char *); // R_ExpandFileName used 42 times in 20 packages void Rf_setIVector(int*, int, int); // Rf_setIVector unused // setIVector unused void Rf_setRVector(double*, int, double); // Rf_setRVector unused // setRVector used 3 times in RcppClassic, RcppClassicExamples Rboolean Rf_StringFalse(const char *); // Rf_StringFalse unused // StringFalse used 3 times in iotools Rboolean Rf_StringTrue(const char *); // Rf_StringTrue unused // StringTrue used 3 times in iotools Rboolean Rf_isBlankString(const char *); // Rf_isBlankString unused // isBlankString used 1 times in iotools double R_atof(const char *str); // R_atof used 9 times in SSN, tree, foreign, iotools double R_strtod(const char *c, char **end); // R_strtod used 4 times in ape, iotools char *R_tmpnam(const char *prefix, const char *tempdir); // R_tmpnam used 2 times in geometry char *R_tmpnam2(const char *prefix, const char *tempdir, const char *fileext); // R_tmpnam2 unused void R_CheckUserInterrupt(void); // R_CheckUserInterrupt used 1487 times in 234 packages void R_CheckStack(void); // R_CheckStack used 115 times in vcrpart, actuar, cplm, lme4, Matrix, GNE, randtoolbox, HiPLARM, rngWELL, pedigreemm void R_CheckStack2(size_t); // R_CheckStack2 unused int findInterval(double *xt, int n, double x, // findInterval used 11 times in BSquare, DNAprofiles, unfoldr, chebpol, pomp, eco, protViz, PBSmapping, spatstat Rboolean rightmost_closed, Rboolean all_inside, int ilo, int *mflag); void find_interv_vec(double *xt, int *n, double *x, int *nx, // find_interv_vec unused int *rightmost_closed, int *all_inside, int *indx); void R_max_col(double *matrix, int *nr, int *nc, int *maxes, int *ties_meth); // R_max_col used 2 times in geostatsp, MNP } # /Users/ls/Source/git/fastr/com.oracle.truffle.r.native/include/R_ext/eventloop.h extern "C" { typedef void (*InputHandlerProc)(void *userData); typedef struct _InputHandler { int activity; int fileDescriptor; InputHandlerProc handler; struct _InputHandler *next; int active; void *userData; } InputHandler; // InputHandler used 36 times in fdaPDE, httpuv, rgl, cairoDevice, setwidth, qtbase, RGtk2 extern InputHandler *initStdinHandler(void); // initStdinHandler unused extern void consoleInputHandler(unsigned char *buf, int len); // consoleInputHandler unused extern InputHandler *addInputHandler(InputHandler *handlers, int fd, InputHandlerProc handler, int activity); // addInputHandler used 10 times in httpuv, rgl, cairoDevice, Cairo, setwidth, rJava, qtbase, RGtk2 extern InputHandler *getInputHandler(InputHandler *handlers, int fd); // getInputHandler unused extern int removeInputHandler(InputHandler **handlers, InputHandler *it); // removeInputHandler used 7 times in httpuv, rgl, cairoDevice, setwidth, qtbase, RGtk2 extern InputHandler *getSelectedHandler(InputHandler *handlers, fd_set *mask); // getSelectedHandler unused extern fd_set *R_checkActivity(int usec, int ignore_stdin); // R_checkActivity used 3 times in audio, rJava, ROracle extern fd_set *R_checkActivityEx(int usec, int ignore_stdin, void (*intr)(void)); extern void R_runHandlers(InputHandler *handlers, fd_set *mask); // R_runHandlers used 2 times in rJava extern int R_SelectEx(int n, fd_set *readfds, fd_set *writefds, fd_set *exceptfds, struct timeval *timeout, void (*intr)(void)); extern InputHandler *R_InputHandlers; extern void (* R_PolledEvents)(void); extern int R_wait_usec; // R_wait_usec unused } # /Users/ls/Source/git/fastr/com.oracle.truffle.r.native/include/R_ext/stats_package.h enum AlgType {NREG = 1, OPT = 2}; enum VPos {F = 9, F0 = 12, FDIF = 10, G = 27, HC = 70}; enum IVPos {AI = 90, AM = 94, ALGSAV = 50, COVMAT = 25, COVPRT = 13, COVREQ = 14, DRADPR = 100, DTYPE = 15, IERR = 74, INITH = 24, INITS = 24, IPIVOT = 75, IVNEED = 2, LASTIV = 42, LASTV = 44, LMAT = 41, MXFCAL = 16, MXITER = 17, NEXTV = 46, NFCALL = 5, NFCOV = 51, NFGCAL = 6, NGCOV = 52, NITER = 30, NVDFLT = 49, NVSAVE = 8, OUTLEV = 18, PARPRT = 19, PARSAV = 48, PERM = 57, PRUNIT = 20, QRTYP = 79, RDREQ = 56, RMAT = 77, SOLPRT = 21, STATPR = 22, TOOBIG = 1, VNEED = 3, VSAVE = 59, X0PRT = 23}; void S_Rf_divset(int alg, int iv[], int liv, int lv, double v[]); void S_nlsb_iterate(double b[], double d[], double dr[], int iv[], int liv, int lv, int n, int nd, int p, double r[], double rd[], double v[], double x[]); void S_nlminb_iterate(double b[], double d[], double fx, double g[], double h[], int iv[], int liv, int lv, int n, double v[], double x[]); static inline int S_v_length(int alg, int n) { return (alg - 1) ? (105 + (n * (2 * n + 20))) : (130 + (n * (n + 27))/2); } static inline int S_iv_length(int alg, int n) { return (alg - 1) ? (82 + 4 * n) : (78 + 3 * n); } # /Users/ls/Source/git/fastr/com.oracle.truffle.r.native/include/R_ext/stats_stubs.h void S_Rf_divset(int alg, int iv[], int liv, int lv, double v[]) { static void(*fun)(int,int[],int,int,double[]) = __null; if (fun == __null) fun = (void(*)(int,int[],int,int,double[])) R_GetCCallable("stats", "Rf_divset"); fun(alg, iv, liv, lv, v); } void S_nlminb_iterate(double b[], double d[], double fx, double g[], double h[], int iv[], int liv, int lv, int n, double v[], double x[]) { static void(*fun)(double[],double[],double,double[],double[], int[],int,int,int,double[],double[]) = __null; if (fun == __null) fun = (void(*)(double[],double[],double,double[],double[], int[],int,int,int,double[],double[])) R_GetCCallable("stats", "nlminb_iterate"); fun(b, d, fx, g, h, iv, liv, lv, n, v, x); } void S_nlsb_iterate(double b[], double d[], double dr[], int iv[], int liv, int lv, int n, int nd, int p, double r[], double rd[], double v[], double x[]) { static void(*fun)(double[],double[],double[],int[],int,int, int,int,int,double[],double[],double[], double[]) = __null; if (fun == __null) fun = (void(*)(double[],double[],double[],int[],int, int, int,int,int,double[], double[],double[],double[])) R_GetCCallable("stats", "nlsb_iterate"); fun(b, d, dr, iv, liv, lv, n, nd, p, r, rd, v, x); } # /Users/ls/Source/git/fastr/com.oracle.truffle.r.native/include/Rembedded.h extern "C" { extern int Rf_initEmbeddedR(int argc, char *argv[]); extern void Rf_endEmbeddedR(int fatal); // Rf_endEmbeddedR used 4 times in RInside, Rhpc, rscproxy, littler int Rf_initialize_R(int ac, char **av); // Rf_initialize_R used 3 times in Rserve, rJava void setup_Rmainloop(void); // setup_Rmainloop used 6 times in Rserve, rJava, rscproxy extern void R_ReplDLLinit(void); // R_ReplDLLinit used 7 times in RInside, Rhpc, rscproxy, Rserve, rJava, littler extern int R_ReplDLLdo1(void); // R_ReplDLLdo1 used 3 times in Rserve, RInside, rJava void R_setStartTime(void); // R_setStartTime unused extern void R_RunExitFinalizers(void); // R_RunExitFinalizers used 4 times in RInside, TMB, rJava, littler extern void CleanEd(void); // CleanEd used 1 times in rJava extern void Rf_KillAllDevices(void); // Rf_KillAllDevices used 1 times in RInside extern int R_DirtyImage; // R_DirtyImage used 1 times in rJava extern void R_CleanTempDir(void); // R_CleanTempDir used 3 times in RInside, sprint, littler extern char *R_TempDir; extern void R_SaveGlobalEnv(void); // R_SaveGlobalEnv used 1 times in rJava void fpu_setup(Rboolean start); // fpu_setup used 3 times in RInside, rJava, littler } # /Users/ls/Source/git/fastr/com.oracle.truffle.r.native/include/Rinterface.h extern "C" { extern Rboolean R_Interactive; // R_Interactive used 16 times in Rhpc, rscproxy, Rserve, RInside, yaml, rJava, littler extern Rboolean R_Slave; // R_Slave used 3 times in Rserve, Rhpc extern void R_RestoreGlobalEnv(void); // R_RestoreGlobalEnv unused extern void R_RestoreGlobalEnvFromFile(const char *, Rboolean); // R_RestoreGlobalEnvFromFile unused extern void R_SaveGlobalEnv(void); // R_SaveGlobalEnv used 1 times in rJava extern void R_SaveGlobalEnvToFile(const char *); // R_SaveGlobalEnvToFile unused extern void R_FlushConsole(void); // R_FlushConsole used 651 times in 78 packages extern void R_ClearerrConsole(void); // R_ClearerrConsole used 2 times in gap, rJava extern void R_Suicide(const char *); // R_Suicide unused extern char *R_HomeDir(void); // R_HomeDir unused extern int R_DirtyImage; // R_DirtyImage used 1 times in rJava extern char *R_GUIType; extern void R_setupHistory(void); // R_setupHistory unused extern char *R_HistoryFile; extern int R_HistorySize; // R_HistorySize used 2 times in rJava extern int R_RestoreHistory; // R_RestoreHistory unused extern char *R_Home; void __attribute__((noreturn)) Rf_jump_to_toplevel(void); void Rf_mainloop(void); // Rf_mainloop unused // mainloop unused void Rf_onintr(void); // Rf_onintr used 216 times in 12 packages // onintr used 1 times in rJava extern void* R_GlobalContext; void process_site_Renviron(void); // process_site_Renviron unused void process_system_Renviron(void); // process_system_Renviron unused void process_user_Renviron(void); // process_user_Renviron unused extern FILE * R_Consolefile; extern FILE * R_Outputfile; void R_setStartTime(void); // R_setStartTime unused void fpu_setup(Rboolean); // fpu_setup used 3 times in RInside, rJava, littler extern int R_running_as_main_program; // R_running_as_main_program unused extern int R_SignalHandlers; // R_SignalHandlers used 5 times in RInside, Rserve, rJava, littler } # /Users/ls/Source/git/fastr/com.oracle.truffle.r.native/include/Rinternals.h extern "C" { typedef unsigned char Rbyte; typedef int R_len_t; // R_len_t used 2397 times in 70 packages typedef ptrdiff_t R_xlen_t; // R_xlen_t used 1537 times in 32 packages typedef struct { R_xlen_t lv_length, lv_truelength; } R_long_vec_hdr_t; typedef unsigned int SEXPTYPE; struct sxpinfo_struct { SEXPTYPE type : 5; unsigned int obj : 1; unsigned int named : 2; unsigned int gp : 16; unsigned int mark : 1; unsigned int debug : 1; unsigned int trace : 1; unsigned int spare : 1; unsigned int gcgen : 1; unsigned int gccls : 3; }; struct vecsxp_struct { R_len_t length; R_len_t truelength; }; struct primsxp_struct { int offset; }; struct symsxp_struct { struct SEXPREC *pname; struct SEXPREC *value; struct SEXPREC *internal; }; struct listsxp_struct { struct SEXPREC *carval; struct SEXPREC *cdrval; struct SEXPREC *tagval; }; struct envsxp_struct { struct SEXPREC *frame; struct SEXPREC *enclos; struct SEXPREC *hashtab; }; struct closxp_struct { struct SEXPREC *formals; struct SEXPREC *body; struct SEXPREC *env; }; struct promsxp_struct { struct SEXPREC *value; struct SEXPREC *expr; struct SEXPREC *env; }; typedef struct SEXPREC { struct sxpinfo_struct sxpinfo; struct SEXPREC *attrib; struct SEXPREC *gengc_next_node, *gengc_prev_node; union { struct primsxp_struct primsxp; struct symsxp_struct symsxp; struct listsxp_struct listsxp; struct envsxp_struct envsxp; struct closxp_struct closxp; struct promsxp_struct promsxp; } u; // u unused } SEXPREC, *SEXP; typedef struct VECTOR_SEXPREC { struct sxpinfo_struct sxpinfo; struct SEXPREC *attrib; struct SEXPREC *gengc_next_node, *gengc_prev_node; struct vecsxp_struct vecsxp; } VECTOR_SEXPREC, *VECSEXP; typedef union { VECTOR_SEXPREC s; double align; } SEXPREC_ALIGN; R_len_t __attribute__((noreturn)) R_BadLongVector(SEXP, const char *, int); SEXP (ATTRIB)(SEXP x); // ATTRIB used 83 times in 20 packages int (OBJECT)(SEXP x); // OBJECT used 102 times in 28 packages int (MARK)(SEXP x); // MARK used 251 times in 21 packages int (TYPEOF)(SEXP x); // TYPEOF used 2832 times in 195 packages int (NAMED)(SEXP x); // NAMED used 62 times in 22 packages int (REFCNT)(SEXP x); // REFCNT unused void (SET_OBJECT)(SEXP x, int v); // SET_OBJECT used 32 times in RSclient, reshape2, Rserve, data.table, actuar, dplyr, proxy, rmongodb, slam, tau void (SET_TYPEOF)(SEXP x, int v); // SET_TYPEOF used 38 times in 21 packages void (SET_NAMED)(SEXP x, int v); // SET_NAMED used 10 times in dplyr, yaml, data.table, iotools, RSQLite void SET_ATTRIB(SEXP x, SEXP v); // SET_ATTRIB used 54 times in 18 packages void DUPLICATE_ATTRIB(SEXP to, SEXP from); // DUPLICATE_ATTRIB used 5 times in covr, lfe, testthat, data.table int (IS_S4_OBJECT)(SEXP x); // IS_S4_OBJECT used 23 times in Rmosek, Runuran, data.table, xts, Matrix, slam, zoo, HiPLARM, OpenMx, tau void (SET_S4_OBJECT)(SEXP x); // SET_S4_OBJECT used 12 times in RSclient, redland, Rserve, data.table, FREGAT, rJPSGCS, tau void (UNSET_S4_OBJECT)(SEXP x); // UNSET_S4_OBJECT used 2 times in data.table, slam int (LENGTH)(SEXP x); // LENGTH used 5845 times in 356 packages int (TRUELENGTH)(SEXP x); // TRUELENGTH used 37 times in data.table void (SETLENGTH)(SEXP x, int v); // SETLENGTH used 65 times in 11 packages void (SET_TRUELENGTH)(SEXP x, int v); // SET_TRUELENGTH used 26 times in data.table R_xlen_t (XLENGTH)(SEXP x); // XLENGTH used 287 times in 21 packages R_xlen_t (XTRUELENGTH)(SEXP x); // XTRUELENGTH unused int (IS_LONG_VEC)(SEXP x); // IS_LONG_VEC used 1 times in RProtoBuf int (LEVELS)(SEXP x); // LEVELS used 18 times in rtdists, rPref, BsMD, data.table, stringi, dplyr, OBsMD, pbdZMQ, astrochron, RandomFields int (SETLEVELS)(SEXP x, int v); // SETLEVELS used 2 times in Rcpp11 int *(LOGICAL)(SEXP x); // LOGICAL used 4473 times in 288 packages int *(INTEGER)(SEXP x); // INTEGER used 41659 times in 758 packages Rbyte *(RAW)(SEXP x); // RAW used 880 times in 99 packages double *(REAL)(SEXP x); // REAL used 30947 times in 687 packages Rcomplex *(COMPLEX)(SEXP x); // COMPLEX used 1697 times in 71 packages SEXP (STRING_ELT)(SEXP x, R_xlen_t i); // STRING_ELT used 4143 times in 333 packages SEXP (VECTOR_ELT)(SEXP x, R_xlen_t i); // VECTOR_ELT used 8626 times in 291 packages void SET_STRING_ELT(SEXP x, R_xlen_t i, SEXP v); // SET_STRING_ELT used 5834 times in 321 packages SEXP SET_VECTOR_ELT(SEXP x, R_xlen_t i, SEXP v); // SET_VECTOR_ELT used 9751 times in 391 packages SEXP *(STRING_PTR)(SEXP x); // STRING_PTR used 65 times in 14 packages SEXP * __attribute__((noreturn)) (VECTOR_PTR)(SEXP x); SEXP (TAG)(SEXP e); // TAG used 513 times in 40 packages SEXP (CAR)(SEXP e); // CAR used 575 times in 63 packages SEXP (CDR)(SEXP e); // CDR used 4523 times in 76 packages SEXP (CAAR)(SEXP e); // CAAR unused SEXP (CDAR)(SEXP e); // CDAR unused SEXP (CADR)(SEXP e); // CADR used 104 times in 17 packages SEXP (CDDR)(SEXP e); // CDDR used 52 times in Rlabkey, Rcpp11, dplyr, proxy, Rcpp, slam, tikzDevice, OpenCL, svd SEXP (CDDDR)(SEXP e); // CDDDR unused SEXP (CADDR)(SEXP e); // CADDR used 52 times in 11 packages SEXP (CADDDR)(SEXP e); // CADDDR used 21 times in RPostgreSQL, foreign, actuar, bibtex SEXP (CAD4R)(SEXP e); // CAD4R used 14 times in earth, foreign, actuar int (MISSING)(SEXP x); // MISSING used 125 times in 25 packages void (SET_MISSING)(SEXP x, int v); // SET_MISSING used 1 times in sprint void SET_TAG(SEXP x, SEXP y); // SET_TAG used 200 times in 34 packages SEXP SETCAR(SEXP x, SEXP y); // SETCAR used 4072 times in 47 packages SEXP SETCDR(SEXP x, SEXP y); // SETCDR used 46 times in 14 packages SEXP SETCADR(SEXP x, SEXP y); // SETCADR used 112 times in 37 packages SEXP SETCADDR(SEXP x, SEXP y); // SETCADDR used 45 times in 14 packages SEXP SETCADDDR(SEXP x, SEXP y); // SETCADDDR used 31 times in 12 packages SEXP SETCAD4R(SEXP e, SEXP y); // SETCAD4R used 15 times in kergp, Sim.DiffProc, tikzDevice SEXP CONS_NR(SEXP a, SEXP b); // CONS_NR unused SEXP (FORMALS)(SEXP x); // FORMALS used 15 times in qtpaint, RSclient, PBSddesolve, Rserve, covr, pryr, rgp, testthat, RandomFields SEXP (BODY)(SEXP x); // BODY used 48 times in 15 packages SEXP (CLOENV)(SEXP x); // CLOENV used 23 times in Rcpp11, covr, pomp, Rcpp, pryr, testthat, qtbase int (RDEBUG)(SEXP x); // RDEBUG used 69 times in rmetasim int (RSTEP)(SEXP x); // RSTEP unused int (RTRACE)(SEXP x); // RTRACE unused void (SET_RDEBUG)(SEXP x, int v); // SET_RDEBUG unused void (SET_RSTEP)(SEXP x, int v); // SET_RSTEP unused void (SET_RTRACE)(SEXP x, int v); // SET_RTRACE unused void SET_FORMALS(SEXP x, SEXP v); // SET_FORMALS used 5 times in covr, rgp, testthat, qtbase void SET_BODY(SEXP x, SEXP v); // SET_BODY used 6 times in covr, rgp, testthat, qtbase void SET_CLOENV(SEXP x, SEXP v); // SET_CLOENV used 6 times in covr, rgp, testthat, qtbase SEXP (PRINTNAME)(SEXP x); // PRINTNAME used 92 times in 29 packages SEXP (SYMVALUE)(SEXP x); // SYMVALUE unused SEXP (INTERNAL)(SEXP x); // INTERNAL used 1014 times in 63 packages int (DDVAL)(SEXP x); // DDVAL unused void (SET_DDVAL)(SEXP x, int v); // SET_DDVAL unused void SET_PRINTNAME(SEXP x, SEXP v); // SET_PRINTNAME unused void SET_SYMVALUE(SEXP x, SEXP v); // SET_SYMVALUE unused void SET_INTERNAL(SEXP x, SEXP v); // SET_INTERNAL unused SEXP (FRAME)(SEXP x); // FRAME used 19 times in deTestSet, IRISSeismic, pryr, BayesBridge, datamap, BayesLogit SEXP (ENCLOS)(SEXP x); // ENCLOS used 7 times in Rcpp, pryr, rJava, Rcpp11, RGtk2 SEXP (HASHTAB)(SEXP x); // HASHTAB used 12 times in Rcpp, pryr, datamap, Rcpp11, qtbase int (ENVFLAGS)(SEXP x); // ENVFLAGS unused void (SET_ENVFLAGS)(SEXP x, int v); // SET_ENVFLAGS unused void SET_FRAME(SEXP x, SEXP v); // SET_FRAME used 4 times in rgp, mmap, qtbase void SET_ENCLOS(SEXP x, SEXP v); // SET_ENCLOS used 7 times in rgp, RandomFields, mmap, qtbase void SET_HASHTAB(SEXP x, SEXP v); // SET_HASHTAB used 5 times in rgp, mmap, qtbase SEXP (PRCODE)(SEXP x); // PRCODE used 15 times in dplyr, Rcpp, pryr, Rcpp11 SEXP (PRENV)(SEXP x); // PRENV used 14 times in igraph, dplyr, Rcpp, pryr, Rcpp11, lazyeval SEXP (PRVALUE)(SEXP x); // PRVALUE used 12 times in dplyr, Rcpp, pryr, Rcpp11 int (PRSEEN)(SEXP x); // PRSEEN used 4 times in Rcpp, Rcpp11 void (SET_PRSEEN)(SEXP x, int v); // SET_PRSEEN unused void SET_PRENV(SEXP x, SEXP v); // SET_PRENV unused void SET_PRVALUE(SEXP x, SEXP v); // SET_PRVALUE unused void SET_PRCODE(SEXP x, SEXP v); // SET_PRCODE unused void SET_PRSEEN(SEXP x, int v); // SET_PRSEEN unused int (HASHASH)(SEXP x); // HASHASH unused int (HASHVALUE)(SEXP x); // HASHVALUE unused void (SET_HASHASH)(SEXP x, int v); // SET_HASHASH unused void (SET_HASHVALUE)(SEXP x, int v); // SET_HASHVALUE unused typedef int PROTECT_INDEX; // PROTECT_INDEX used 94 times in 27 packages extern SEXP R_GlobalEnv; // R_GlobalEnv used 1400 times in 79 packages extern SEXP R_EmptyEnv; // R_EmptyEnv used 16 times in Rserve, dplR, Rcpp11, Rcpp, RcppClassic, pryr, rJava, adaptivetau, qtbase extern SEXP R_BaseEnv; // R_BaseEnv used 27 times in 15 packages extern SEXP R_BaseNamespace; // R_BaseNamespace used 3 times in Rcpp, Rcpp11 extern SEXP R_NamespaceRegistry; // R_NamespaceRegistry used 3 times in devtools, namespace, Rcpp extern SEXP R_Srcref; // R_Srcref unused extern SEXP R_NilValue; // R_NilValue used 10178 times in 491 packages // NULL_USER_OBJECT used 8268 times in rggobi, XML, rjson, bigmemory, dbarts, lazy, RGtk2 extern SEXP R_UnboundValue; // R_UnboundValue used 73 times in 23 packages extern SEXP R_MissingArg; // R_MissingArg used 21 times in 12 packages extern SEXP R_RestartToken; // R_RestartToken unused extern SEXP R_baseSymbol; // R_baseSymbol unused extern SEXP R_BaseSymbol; // R_BaseSymbol unused extern SEXP R_BraceSymbol; // R_BraceSymbol unused extern SEXP R_Bracket2Symbol; // R_Bracket2Symbol used 4 times in purrr extern SEXP R_BracketSymbol; // R_BracketSymbol unused extern SEXP R_ClassSymbol; // R_ClassSymbol used 311 times in 84 packages extern SEXP R_DeviceSymbol; // R_DeviceSymbol unused extern SEXP R_DimNamesSymbol; // R_DimNamesSymbol used 230 times in 51 packages extern SEXP R_DimSymbol; // R_DimSymbol used 1015 times in 170 packages extern SEXP R_DollarSymbol; // R_DollarSymbol used 6 times in dplyr, Rcpp, Rcpp11 extern SEXP R_DotsSymbol; // R_DotsSymbol used 13 times in RPostgreSQL, RcppDE, lbfgs, purrr, RMySQL, DEoptim, qtbase extern SEXP R_DoubleColonSymbol; // R_DoubleColonSymbol unused extern SEXP R_DropSymbol; // R_DropSymbol unused extern SEXP R_LastvalueSymbol; // R_LastvalueSymbol unused extern SEXP R_LevelsSymbol; // R_LevelsSymbol used 51 times in 17 packages extern SEXP R_ModeSymbol; // R_ModeSymbol unused extern SEXP R_NaRmSymbol; // R_NaRmSymbol used 2 times in dplyr extern SEXP R_NameSymbol; // R_NameSymbol used 2 times in qtbase extern SEXP R_NamesSymbol; // R_NamesSymbol used 1373 times in 249 packages extern SEXP R_NamespaceEnvSymbol; // R_NamespaceEnvSymbol unused extern SEXP R_PackageSymbol; // R_PackageSymbol used 2 times in Rmosek, HiPLARM extern SEXP R_PreviousSymbol; // R_PreviousSymbol unused extern SEXP R_QuoteSymbol; // R_QuoteSymbol unused extern SEXP R_RowNamesSymbol; // R_RowNamesSymbol used 97 times in 37 packages extern SEXP R_SeedsSymbol; // R_SeedsSymbol used 2 times in treatSens extern SEXP R_SortListSymbol; // R_SortListSymbol unused extern SEXP R_SourceSymbol; // R_SourceSymbol unused extern SEXP R_SpecSymbol; // R_SpecSymbol unused extern SEXP R_TripleColonSymbol; // R_TripleColonSymbol unused extern SEXP R_TspSymbol; // R_TspSymbol unused extern SEXP R_dot_defined; // R_dot_defined unused extern SEXP R_dot_Method; // R_dot_Method unused extern SEXP R_dot_packageName; // R_dot_packageName unused extern SEXP R_dot_target; // R_dot_target unused extern SEXP R_NaString; // R_NaString used 36 times in stringdist, RCurl, RSclient, uniqueAtomMat, XML, Rserve, Rblpapi, SoundexBR, rJava, iotools // NA_STRING used 574 times in 90 packages extern SEXP R_BlankString; // R_BlankString used 39 times in 13 packages extern SEXP R_BlankScalarString; // R_BlankScalarString unused SEXP R_GetCurrentSrcref(int); // R_GetCurrentSrcref unused SEXP R_GetSrcFilename(SEXP); // R_GetSrcFilename unused SEXP Rf_asChar(SEXP); // Rf_asChar used 246 times in 16 packages // asChar used 194 times in 36 packages SEXP Rf_coerceVector(SEXP, SEXPTYPE); // Rf_coerceVector used 44 times in 13 packages // coerceVector used 2585 times in 167 packages SEXP Rf_PairToVectorList(SEXP x); // Rf_PairToVectorList unused // PairToVectorList used 7 times in cba, rcdd SEXP Rf_VectorToPairList(SEXP x); // Rf_VectorToPairList unused // VectorToPairList used 13 times in pomp, arules SEXP Rf_asCharacterFactor(SEXP x); // Rf_asCharacterFactor used 3 times in tidyr, reshape2, RSQLite // asCharacterFactor used 11 times in fastmatch, Kmisc, data.table int Rf_asLogical(SEXP x); // Rf_asLogical used 45 times in 11 packages // asLogical used 462 times in 64 packages int Rf_asInteger(SEXP x); // Rf_asInteger used 746 times in 23 packages // asInteger used 1277 times in 140 packages double Rf_asReal(SEXP x); // Rf_asReal used 113 times in 17 packages // asReal used 383 times in 83 packages Rcomplex Rf_asComplex(SEXP x); // Rf_asComplex unused // asComplex used 1 times in ff typedef struct R_allocator R_allocator_t; char * Rf_acopy_string(const char *); // Rf_acopy_string unused // acopy_string used 10 times in splusTimeDate void Rf_addMissingVarsToNewEnv(SEXP, SEXP); // Rf_addMissingVarsToNewEnv unused // addMissingVarsToNewEnv unused SEXP Rf_alloc3DArray(SEXPTYPE, int, int, int); // Rf_alloc3DArray unused // alloc3DArray used 21 times in mcmc, msm, TPmsm, unfoldr, RandomFields, cplm SEXP Rf_allocArray(SEXPTYPE, SEXP); // Rf_allocArray used 4 times in h5 // allocArray used 24 times in unfoldr, kergp, pomp, proxy, kza, slam, mvMORPH, TPmsm, ouch, RandomFields SEXP Rf_allocFormalsList2(SEXP sym1, SEXP sym2); // Rf_allocFormalsList2 unused // allocFormalsList2 unused SEXP Rf_allocFormalsList3(SEXP sym1, SEXP sym2, SEXP sym3); // Rf_allocFormalsList3 unused // allocFormalsList3 unused SEXP Rf_allocFormalsList4(SEXP sym1, SEXP sym2, SEXP sym3, SEXP sym4); // Rf_allocFormalsList4 unused // allocFormalsList4 unused SEXP Rf_allocFormalsList5(SEXP sym1, SEXP sym2, SEXP sym3, SEXP sym4, SEXP sym5); // Rf_allocFormalsList5 unused // allocFormalsList5 unused SEXP Rf_allocFormalsList6(SEXP sym1, SEXP sym2, SEXP sym3, SEXP sym4, SEXP sym5, SEXP sym6); // Rf_allocFormalsList6 unused // allocFormalsList6 unused SEXP Rf_allocMatrix(SEXPTYPE, int, int); // Rf_allocMatrix used 122 times in 14 packages // allocMatrix used 1577 times in 244 packages SEXP Rf_allocList(int); // Rf_allocList unused // allocList used 60 times in 25 packages SEXP Rf_allocS4Object(void); // Rf_allocS4Object used 2 times in Rserve, RSclient // allocS4Object used 1 times in arules SEXP Rf_allocSExp(SEXPTYPE); // Rf_allocSExp unused // allocSExp used 14 times in igraph, rgp, data.table, RandomFields, mmap, qtbase SEXP Rf_allocVector3(SEXPTYPE, R_xlen_t, R_allocator_t*); // Rf_allocVector3 unused // allocVector3 unused R_xlen_t Rf_any_duplicated(SEXP x, Rboolean from_last); // Rf_any_duplicated unused // any_duplicated used 5 times in data.table, checkmate R_xlen_t Rf_any_duplicated3(SEXP x, SEXP incomp, Rboolean from_last); // Rf_any_duplicated3 unused // any_duplicated3 unused SEXP Rf_applyClosure(SEXP, SEXP, SEXP, SEXP, SEXP); // Rf_applyClosure unused // applyClosure unused SEXP Rf_arraySubscript(int, SEXP, SEXP, SEXP (*)(SEXP,SEXP), SEXP (*)(SEXP, int), SEXP); SEXP Rf_classgets(SEXP, SEXP); // Rf_classgets used 27 times in fts, clpAPI, cplexAPI, sybilSBML, Rblpapi, glpkAPI // classgets used 91 times in 30 packages SEXP Rf_cons(SEXP, SEXP); // Rf_cons used 39 times in dplyr, Rcpp, Rcpp11 // cons used 609 times in 39 packages void Rf_copyMatrix(SEXP, SEXP, Rboolean); // Rf_copyMatrix used 8 times in CNVassoc // copyMatrix used 7 times in BDgraph, Matrix, kza void Rf_copyListMatrix(SEXP, SEXP, Rboolean); // Rf_copyListMatrix unused // copyListMatrix used 1 times in Matrix void Rf_copyMostAttrib(SEXP, SEXP); // Rf_copyMostAttrib used 8 times in tidyr, purrr, Rcpp, reshape2 // copyMostAttrib used 68 times in arules, robustbase, data.table, xts, memisc, proxy, zoo, tau void Rf_copyVector(SEXP, SEXP); // Rf_copyVector unused // copyVector used 12 times in tm, kza, mlegp, adaptivetau int Rf_countContexts(int, int); // Rf_countContexts unused // countContexts unused SEXP Rf_CreateTag(SEXP); // Rf_CreateTag unused // CreateTag used 1 times in rgp void Rf_defineVar(SEXP, SEXP, SEXP); // Rf_defineVar used 7 times in purrr, Rcpp, Rserve, Rcpp11 // defineVar used 218 times in 38 packages SEXP Rf_dimgets(SEXP, SEXP); // Rf_dimgets unused // dimgets used 3 times in CorrBin SEXP Rf_dimnamesgets(SEXP, SEXP); // Rf_dimnamesgets unused // dimnamesgets used 24 times in Matrix, RxCEcolInf, lxb, sapa SEXP Rf_DropDims(SEXP); // Rf_DropDims unused // DropDims unused SEXP Rf_duplicate(SEXP); // Rf_duplicate used 21 times in XML, data.table, Rcpp11, lme4, dplyr, Rcpp, RcppClassic, grr, NMF, copula // duplicate used 2088 times in 224 packages SEXP Rf_shallow_duplicate(SEXP); // Rf_shallow_duplicate unused // shallow_duplicate used 2 times in tmlenet, smint SEXP Rf_lazy_duplicate(SEXP); // Rf_lazy_duplicate unused // lazy_duplicate unused SEXP Rf_duplicated(SEXP, Rboolean); // Rf_duplicated unused // duplicated used 402 times in 100 packages Rboolean R_envHasNoSpecialSymbols(SEXP); // R_envHasNoSpecialSymbols unused SEXP Rf_eval(SEXP, SEXP); // Rf_eval used 105 times in 24 packages // eval used 25178 times in 269 packages SEXP Rf_findFun(SEXP, SEXP); // Rf_findFun used 7 times in Rcpp, Rcpp11, littler, RGtk2 // findFun used 13 times in sprint, tikzDevice, yaml, unfoldr, TraMineR, RGtk2 SEXP Rf_findVar(SEXP, SEXP); // Rf_findVar used 19 times in R2SWF, Rcpp11, dplyr, Rcpp, pryr, rJava, littler, showtext // findVar used 1333 times in 24 packages SEXP Rf_findVarInFrame(SEXP, SEXP); // Rf_findVarInFrame used 7 times in RCurl, Rcpp, Rcpp11 // findVarInFrame used 101 times in 13 packages SEXP Rf_findVarInFrame3(SEXP, SEXP, Rboolean); // Rf_findVarInFrame3 used 1 times in pryr // findVarInFrame3 used 5 times in datamap SEXP Rf_getAttrib(SEXP, SEXP); // Rf_getAttrib used 256 times in 36 packages // getAttrib used 1930 times in 239 packages SEXP Rf_GetArrayDimnames(SEXP); // Rf_GetArrayDimnames unused // GetArrayDimnames unused SEXP Rf_GetColNames(SEXP); // Rf_GetColNames unused // GetColNames unused void Rf_GetMatrixDimnames(SEXP, SEXP*, SEXP*, const char**, const char**); // Rf_GetMatrixDimnames unused // GetMatrixDimnames used 2 times in Kmisc, optmatch SEXP Rf_GetOption(SEXP, SEXP); // Rf_GetOption unused // GetOption used 5 times in rgl, gmp, Cairo, RGtk2 SEXP Rf_GetOption1(SEXP); // Rf_GetOption1 used 5 times in RProtoBuf, gmp // GetOption1 used 1 times in PCICt int Rf_GetOptionDigits(void); // Rf_GetOptionDigits unused // GetOptionDigits unused int Rf_GetOptionWidth(void); // Rf_GetOptionWidth used 1 times in progress // GetOptionWidth unused SEXP Rf_GetRowNames(SEXP); // Rf_GetRowNames unused // GetRowNames unused void Rf_gsetVar(SEXP, SEXP, SEXP); // Rf_gsetVar unused // gsetVar used 4 times in RSVGTipsDevice, Cairo, RSvgDevice, JavaGD SEXP Rf_install(const char *); // Rf_install used 990 times in 50 packages // install used 3178 times in 224 packages SEXP Rf_installChar(SEXP); // Rf_installChar used 15 times in dplyr, Rcpp // installChar used 4 times in dplyr SEXP Rf_installDDVAL(int i); // Rf_installDDVAL unused // installDDVAL unused SEXP Rf_installS3Signature(const char *, const char *); // Rf_installS3Signature unused // installS3Signature unused Rboolean Rf_isFree(SEXP); // Rf_isFree unused // isFree unused Rboolean Rf_isOrdered(SEXP); // Rf_isOrdered unused // isOrdered used 65 times in partykit, PythonInR, data.table, RSQLite Rboolean Rf_isUnordered(SEXP); // Rf_isUnordered used 1 times in OpenMx // isUnordered used 2 times in PythonInR Rboolean Rf_isUnsorted(SEXP, Rboolean); // Rf_isUnsorted unused // isUnsorted unused SEXP Rf_lengthgets(SEXP, R_len_t); // Rf_lengthgets used 7 times in readxl, readr // lengthgets used 47 times in 11 packages SEXP Rf_xlengthgets(SEXP, R_xlen_t); // Rf_xlengthgets unused // xlengthgets unused SEXP R_lsInternal(SEXP, Rboolean); // R_lsInternal used 5 times in Rcpp, rJava, Rcpp11, qtbase SEXP R_lsInternal3(SEXP, Rboolean, Rboolean); // R_lsInternal3 unused SEXP Rf_match(SEXP, SEXP, int); // Rf_match used 2 times in Rvcg // match used 8773 times in 388 packages SEXP Rf_matchE(SEXP, SEXP, int, SEXP); // Rf_matchE unused // matchE unused SEXP Rf_namesgets(SEXP, SEXP); // Rf_namesgets used 4 times in OpenMx, rpf // namesgets used 80 times in 14 packages SEXP Rf_mkChar(const char *); // Rf_mkChar used 517 times in 32 packages // mkChar used 4545 times in 287 packages SEXP Rf_mkCharLen(const char *, int); // Rf_mkCharLen used 21 times in refGenome, redland, Rcpp11, stringi, Kmisc, Rcpp, sourcetools, iotools // mkCharLen used 38 times in 16 packages Rboolean Rf_NonNullStringMatch(SEXP, SEXP); // Rf_NonNullStringMatch unused // NonNullStringMatch used 8 times in proxy, arules, arulesSequences, cba int Rf_ncols(SEXP); // Rf_ncols used 22 times in fdaPDE, fts, BoomSpikeSlab, Rmosek, ccgarch, rcppbugs, biganalytics, CEC, OpenMx, RTriangle // ncols used 3805 times in 182 packages int Rf_nrows(SEXP); // Rf_nrows used 32 times in 12 packages // nrows used 4332 times in 215 packages SEXP Rf_nthcdr(SEXP, int); // Rf_nthcdr unused // nthcdr used 9 times in sprint, rmongodb, PythonInR, xts typedef enum {Bytes, Chars, Width} nchar_type; int R_nchar(SEXP string, nchar_type type_, // R_nchar unused Rboolean allowNA, Rboolean keepNA, const char* msg_name); Rboolean Rf_pmatch(SEXP, SEXP, Rboolean); // Rf_pmatch unused // pmatch used 169 times in ore, git2r, AdaptFitOS, data.table, seqminer, locfit, oce, rmumps Rboolean Rf_psmatch(const char *, const char *, Rboolean); // Rf_psmatch unused // psmatch used 5 times in rgl void Rf_PrintValue(SEXP); // Rf_PrintValue used 95 times in 19 packages // PrintValue used 119 times in 13 packages void Rf_readS3VarsFromFrame(SEXP, SEXP*, SEXP*, SEXP*, SEXP*, SEXP*, SEXP*); // Rf_readS3VarsFromFrame unused // readS3VarsFromFrame unused SEXP Rf_setAttrib(SEXP, SEXP, SEXP); // Rf_setAttrib used 325 times in 35 packages // setAttrib used 1830 times in 251 packages void Rf_setSVector(SEXP*, int, SEXP); // Rf_setSVector unused // setSVector unused void Rf_setVar(SEXP, SEXP, SEXP); // Rf_setVar used 1 times in showtext // setVar used 24 times in Rhpc, rscproxy, PythonInR, rgenoud, survival, gsl, littler, spatstat SEXP Rf_stringSuffix(SEXP, int); // Rf_stringSuffix unused // stringSuffix unused SEXPTYPE Rf_str2type(const char *); // Rf_str2type used 4 times in purrr // str2type used 1 times in RGtk2 Rboolean Rf_StringBlank(SEXP); // Rf_StringBlank used 1 times in LCMCR // StringBlank unused SEXP Rf_substitute(SEXP,SEXP); // Rf_substitute unused // substitute used 255 times in 56 packages const char * Rf_translateChar(SEXP); // Rf_translateChar used 1 times in devEMF // translateChar used 59 times in 19 packages const char * Rf_translateChar0(SEXP); // Rf_translateChar0 unused // translateChar0 unused const char * Rf_translateCharUTF8(SEXP); // Rf_translateCharUTF8 used 22 times in Rserve, xml2, readr, gdtools, Rcpp11, dplyr, Rcpp, haven // translateCharUTF8 used 66 times in 13 packages const char * Rf_type2char(SEXPTYPE); // Rf_type2char used 33 times in 13 packages // type2char used 107 times in 12 packages SEXP Rf_type2rstr(SEXPTYPE); // Rf_type2rstr unused // type2rstr unused SEXP Rf_type2str(SEXPTYPE); // Rf_type2str used 4 times in Rcpp, pryr // type2str used 3 times in Kmisc, yaml SEXP Rf_type2str_nowarn(SEXPTYPE); // Rf_type2str_nowarn unused // type2str_nowarn used 1 times in qrmtools void Rf_unprotect_ptr(SEXP); // Rf_unprotect_ptr unused // unprotect_ptr unused void __attribute__((noreturn)) R_signal_protect_error(void); void __attribute__((noreturn)) R_signal_unprotect_error(void); void __attribute__((noreturn)) R_signal_reprotect_error(PROTECT_INDEX i); SEXP R_tryEval(SEXP, SEXP, int *); // R_tryEval used 1118 times in 24 packages SEXP R_tryEvalSilent(SEXP, SEXP, int *); // R_tryEvalSilent unused const char *R_curErrorBuf(); // R_curErrorBuf used 4 times in Rhpc, Rcpp11 Rboolean Rf_isS4(SEXP); // Rf_isS4 used 16 times in Rcpp, Rcpp11 // isS4 used 13 times in PythonInR, Rcpp11, dplyr, Rcpp, catnet, rmumps, sdnet SEXP Rf_asS4(SEXP, Rboolean, int); // Rf_asS4 unused // asS4 unused SEXP Rf_S3Class(SEXP); // Rf_S3Class unused // S3Class used 4 times in RInside, littler int Rf_isBasicClass(const char *); // Rf_isBasicClass unused // isBasicClass unused Rboolean R_cycle_detected(SEXP s, SEXP child); // R_cycle_detected unused typedef enum { CE_NATIVE = 0, CE_UTF8 = 1, CE_LATIN1 = 2, CE_BYTES = 3, CE_SYMBOL = 5, CE_ANY =99 } cetype_t; // cetype_t used 47 times in 13 packages cetype_t Rf_getCharCE(SEXP); // Rf_getCharCE used 13 times in RSclient, Rserve, genie, dplyr, Rcpp, rJava, ROracle // getCharCE used 16 times in ore, RSclient, PythonInR, Rserve, jsonlite, tau, rJava SEXP Rf_mkCharCE(const char *, cetype_t); // Rf_mkCharCE used 40 times in readxl, mongolite, xml2, readr, Rcpp11, stringi, commonmark, dplyr, Rcpp, haven // mkCharCE used 72 times in 15 packages SEXP Rf_mkCharLenCE(const char *, int, cetype_t); // Rf_mkCharLenCE used 68 times in readr, ROracle, stringi // mkCharLenCE used 23 times in 11 packages const char *Rf_reEnc(const char *x, cetype_t ce_in, cetype_t ce_out, int subst); // Rf_reEnc used 5 times in RCurl, RSclient, Rserve, rJava // reEnc used 3 times in PythonInR, RJSONIO SEXP R_forceAndCall(SEXP e, int n, SEXP rho); // R_forceAndCall unused SEXP R_MakeExternalPtr(void *p, SEXP tag, SEXP prot); // R_MakeExternalPtr used 321 times in 102 packages void *R_ExternalPtrAddr(SEXP s); // R_ExternalPtrAddr used 2127 times in 115 packages SEXP R_ExternalPtrTag(SEXP s); // R_ExternalPtrTag used 195 times in 32 packages SEXP R_ExternalPtrProtected(SEXP s); // R_ExternalPtrProtected used 6 times in PopGenome, Rcpp, WhopGenome, data.table, Rcpp11 void R_ClearExternalPtr(SEXP s); // R_ClearExternalPtr used 157 times in 64 packages void R_SetExternalPtrAddr(SEXP s, void *p); // R_SetExternalPtrAddr used 23 times in ff, PopGenome, RCurl, rstream, Rlabkey, WhopGenome, XML, RJSONIO, memisc, ROracle void R_SetExternalPtrTag(SEXP s, SEXP tag); // R_SetExternalPtrTag used 16 times in PopGenome, rstream, Rlabkey, WhopGenome, Rcpp11, Rcpp, rLindo void R_SetExternalPtrProtected(SEXP s, SEXP p); // R_SetExternalPtrProtected used 9 times in PopGenome, rstream, Rlabkey, Rcpp, WhopGenome, Rcpp11 typedef void (*R_CFinalizer_t)(SEXP); void R_RegisterFinalizer(SEXP s, SEXP fun); // R_RegisterFinalizer used 1 times in XML void R_RegisterCFinalizer(SEXP s, R_CFinalizer_t fun); // R_RegisterCFinalizer used 73 times in 27 packages void R_RegisterFinalizerEx(SEXP s, SEXP fun, Rboolean onexit); // R_RegisterFinalizerEx unused void R_RegisterCFinalizerEx(SEXP s, R_CFinalizer_t fun, Rboolean onexit); // R_RegisterCFinalizerEx used 152 times in 58 packages void R_RunPendingFinalizers(void); // R_RunPendingFinalizers unused SEXP R_MakeWeakRef(SEXP key, SEXP val, SEXP fin, Rboolean onexit); // R_MakeWeakRef used 4 times in igraph, svd SEXP R_MakeWeakRefC(SEXP key, SEXP val, R_CFinalizer_t fin, Rboolean onexit); // R_MakeWeakRefC unused SEXP R_WeakRefKey(SEXP w); // R_WeakRefKey used 3 times in igraph, Rcpp, Rcpp11 SEXP R_WeakRefValue(SEXP w); // R_WeakRefValue used 7 times in igraph, Rcpp, svd, Rcpp11 void R_RunWeakRefFinalizer(SEXP w); // R_RunWeakRefFinalizer used 1 times in igraph SEXP R_PromiseExpr(SEXP); // R_PromiseExpr unused SEXP R_ClosureExpr(SEXP); // R_ClosureExpr unused void R_initialize_bcode(void); // R_initialize_bcode unused SEXP R_bcEncode(SEXP); // R_bcEncode unused SEXP R_bcDecode(SEXP); // R_bcDecode unused Rboolean R_ToplevelExec(void (*fun)(void *), void *data); SEXP R_ExecWithCleanup(SEXP (*fun)(void *), void *data, void (*cleanfun)(void *), void *cleandata); void R_RestoreHashCount(SEXP rho); // R_RestoreHashCount unused Rboolean R_IsPackageEnv(SEXP rho); // R_IsPackageEnv unused SEXP R_PackageEnvName(SEXP rho); // R_PackageEnvName unused SEXP R_FindPackageEnv(SEXP info); // R_FindPackageEnv unused Rboolean R_IsNamespaceEnv(SEXP rho); // R_IsNamespaceEnv unused SEXP R_NamespaceEnvSpec(SEXP rho); // R_NamespaceEnvSpec unused SEXP R_FindNamespace(SEXP info); // R_FindNamespace used 14 times in 11 packages void R_LockEnvironment(SEXP env, Rboolean bindings); // R_LockEnvironment used 2 times in Rcpp, Rcpp11 Rboolean R_EnvironmentIsLocked(SEXP env); // R_EnvironmentIsLocked used 2 times in Rcpp, Rcpp11 void R_LockBinding(SEXP sym, SEXP env); // R_LockBinding used 3 times in data.table, Rcpp, Rcpp11 void R_unLockBinding(SEXP sym, SEXP env); // R_unLockBinding used 2 times in Rcpp, Rcpp11 void R_MakeActiveBinding(SEXP sym, SEXP fun, SEXP env); // R_MakeActiveBinding unused Rboolean R_BindingIsLocked(SEXP sym, SEXP env); // R_BindingIsLocked used 2 times in Rcpp, Rcpp11 Rboolean R_BindingIsActive(SEXP sym, SEXP env); // R_BindingIsActive used 2 times in Rcpp, Rcpp11 Rboolean R_HasFancyBindings(SEXP rho); // R_HasFancyBindings unused void Rf_errorcall(SEXP, const char *, ...) __attribute__((noreturn)); // Rf_errorcall used 27 times in purrr, mongolite, jsonlite, pbdMPI, rJava, openssl // errorcall used 103 times in RCurl, arules, XML, arulesSequences, pbdMPI, xts, proxy, cba, rJava, RSAP void Rf_warningcall(SEXP, const char *, ...); // Rf_warningcall used 5 times in pbdMPI, mongolite // warningcall used 4 times in RInside, jsonlite, pbdMPI void Rf_warningcall_immediate(SEXP, const char *, ...); // Rf_warningcall_immediate used 2 times in mongolite, V8 // warningcall_immediate used 2 times in Runuran void R_XDREncodeDouble(double d, void *buf); // R_XDREncodeDouble unused double R_XDRDecodeDouble(void *buf); // R_XDRDecodeDouble unused void R_XDREncodeInteger(int i, void *buf); // R_XDREncodeInteger unused int R_XDRDecodeInteger(void *buf); // R_XDRDecodeInteger unused typedef void *R_pstream_data_t; typedef enum { R_pstream_any_format, R_pstream_ascii_format, R_pstream_binary_format, R_pstream_xdr_format, R_pstream_asciihex_format } R_pstream_format_t; // R_pstream_format_t used 7 times in RApiSerialize, Rhpc, fastdigest typedef struct R_outpstream_st *R_outpstream_t; struct R_outpstream_st { R_pstream_data_t data; R_pstream_format_t type; int version; void (*OutChar)(R_outpstream_t, int); void (*OutBytes)(R_outpstream_t, void *, int); SEXP (*OutPersistHookFunc)(SEXP, SEXP); SEXP OutPersistHookData; // OutPersistHookData unused }; typedef struct R_inpstream_st *R_inpstream_t; struct R_inpstream_st { R_pstream_data_t data; R_pstream_format_t type; int (*InChar)(R_inpstream_t); void (*InBytes)(R_inpstream_t, void *, int); SEXP (*InPersistHookFunc)(SEXP, SEXP); SEXP InPersistHookData; // InPersistHookData unused }; void R_InitInPStream(R_inpstream_t stream, R_pstream_data_t data, // R_InitInPStream used 2 times in RApiSerialize, Rhpc R_pstream_format_t type, int (*inchar)(R_inpstream_t), void (*inbytes)(R_inpstream_t, void *, int), SEXP (*phook)(SEXP, SEXP), SEXP pdata); void R_InitOutPStream(R_outpstream_t stream, R_pstream_data_t data, // R_InitOutPStream used 4 times in RApiSerialize, Rhpc, fastdigest, qtbase R_pstream_format_t type, int version, void (*outchar)(R_outpstream_t, int), void (*outbytes)(R_outpstream_t, void *, int), SEXP (*phook)(SEXP, SEXP), SEXP pdata); void R_InitFileInPStream(R_inpstream_t stream, FILE *fp, // R_InitFileInPStream used 1 times in filehash R_pstream_format_t type, SEXP (*phook)(SEXP, SEXP), SEXP pdata); void R_InitFileOutPStream(R_outpstream_t stream, FILE *fp, // R_InitFileOutPStream unused R_pstream_format_t type, int version, SEXP (*phook)(SEXP, SEXP), SEXP pdata); void R_Serialize(SEXP s, R_outpstream_t ops); // R_Serialize used 4 times in RApiSerialize, Rhpc, fastdigest, qtbase SEXP R_Unserialize(R_inpstream_t ips); // R_Unserialize used 4 times in RApiSerialize, Rhpc, filehash SEXP R_do_slot(SEXP obj, SEXP name); // R_do_slot used 162 times in 11 packages SEXP R_do_slot_assign(SEXP obj, SEXP name, SEXP value); // R_do_slot_assign used 17 times in excel.link, redland, Rcpp11, Matrix, TMB, Rcpp, FREGAT, HiPLARM, OpenMx, rJPSGCS int R_has_slot(SEXP obj, SEXP name); // R_has_slot used 14 times in Matrix, Rcpp, HiPLARM, OpenMx, Rcpp11 SEXP R_do_MAKE_CLASS(const char *what); // R_do_MAKE_CLASS used 6 times in TMB, Rcpp, Rcpp11 SEXP R_getClassDef (const char *what); // R_getClassDef used 5 times in memisc, Rcpp, Rcpp11 SEXP R_getClassDef_R(SEXP what); // R_getClassDef_R unused Rboolean R_has_methods_attached(void); // R_has_methods_attached unused Rboolean R_isVirtualClass(SEXP class_def, SEXP env); // R_isVirtualClass unused Rboolean R_extends (SEXP class1, SEXP class2, SEXP env); // R_extends unused SEXP R_do_new_object(SEXP class_def); // R_do_new_object used 9 times in TMB, memisc, Rcpp, Rcpp11 int R_check_class_and_super(SEXP x, const char **valid, SEXP rho); // R_check_class_and_super used 5 times in Matrix, Rmosek, HiPLARM int R_check_class_etc (SEXP x, const char **valid); // R_check_class_etc used 41 times in Matrix, HiPLARM void R_PreserveObject(SEXP); // R_PreserveObject used 112 times in 29 packages void R_ReleaseObject(SEXP); // R_ReleaseObject used 114 times in 27 packages void R_dot_Last(void); // R_dot_Last used 4 times in RInside, rJava, littler void R_RunExitFinalizers(void); // R_RunExitFinalizers used 4 times in RInside, TMB, rJava, littler int R_system(const char *); // R_system used 1 times in rJava Rboolean R_compute_identical(SEXP, SEXP, int); // R_compute_identical used 14 times in igraph, Matrix, rgp, data.table void R_orderVector(int *indx, int n, SEXP arglist, Rboolean nalast, Rboolean decreasing); // R_orderVector used 5 times in glpkAPI, nontarget, CEGO SEXP Rf_allocVector(SEXPTYPE, R_xlen_t); // Rf_allocVector used 1086 times in 59 packages // allocVector used 12419 times in 551 packages Rboolean Rf_conformable(SEXP, SEXP); // Rf_conformable unused // conformable used 141 times in 22 packages SEXP Rf_elt(SEXP, int); // Rf_elt unused // elt used 2310 times in 37 packages Rboolean Rf_inherits(SEXP, const char *); // Rf_inherits used 530 times in 21 packages // inherits used 814 times in 80 packages Rboolean Rf_isArray(SEXP); // Rf_isArray unused // isArray used 34 times in checkmate, PythonInR, data.table, ifultools, Rblpapi, Rvcg, unfoldr, TMB, kza, qtbase Rboolean Rf_isFactor(SEXP); // Rf_isFactor used 22 times in 11 packages // isFactor used 42 times in checkmate, rggobi, PythonInR, data.table, Kmisc, partykit, cba, qtbase, RSQLite Rboolean Rf_isFrame(SEXP); // Rf_isFrame used 1 times in OpenMx // isFrame used 15 times in checkmate, splusTimeDate, OjaNP, PythonInR, data.table, robfilter Rboolean Rf_isFunction(SEXP); // Rf_isFunction used 4 times in Rserve, genie, RcppClassic // isFunction used 274 times in 43 packages Rboolean Rf_isInteger(SEXP); // Rf_isInteger used 39 times in 14 packages // isInteger used 402 times in 77 packages Rboolean Rf_isLanguage(SEXP); // Rf_isLanguage unused // isLanguage used 63 times in PythonInR, rgp, RandomFields Rboolean Rf_isList(SEXP); // Rf_isList unused // isList used 40 times in 11 packages Rboolean Rf_isMatrix(SEXP); // Rf_isMatrix used 55 times in 16 packages // isMatrix used 293 times in 65 packages Rboolean Rf_isNewList(SEXP); // Rf_isNewList used 6 times in Rmosek, RcppClassic // isNewList used 103 times in 27 packages Rboolean Rf_isNumber(SEXP); // Rf_isNumber unused // isNumber used 14 times in PythonInR, readr, stringi, qtbase Rboolean Rf_isNumeric(SEXP); // Rf_isNumeric used 31 times in Rmosek, gaselect, RcppCNPy, genie, mets, Morpho, rstan, Rcpp, RcppClassic, OpenMx // isNumeric used 468 times in 49 packages Rboolean Rf_isPairList(SEXP); // Rf_isPairList unused // isPairList used 2 times in PythonInR Rboolean Rf_isPrimitive(SEXP); // Rf_isPrimitive unused // isPrimitive used 7 times in PythonInR, qtbase Rboolean Rf_isTs(SEXP); // Rf_isTs unused // isTs used 2 times in PythonInR Rboolean Rf_isUserBinop(SEXP); // Rf_isUserBinop unused // isUserBinop used 2 times in PythonInR Rboolean Rf_isValidString(SEXP); // Rf_isValidString unused // isValidString used 26 times in SSN, PythonInR, foreign, pbdMPI, RJSONIO, SASxport Rboolean Rf_isValidStringF(SEXP); // Rf_isValidStringF unused // isValidStringF used 2 times in PythonInR Rboolean Rf_isVector(SEXP); // Rf_isVector used 15 times in RProtoBuf, RcppCNPy, stringi, purrr, RcppClassic, OpenMx, adaptivetau // isVector used 182 times in 46 packages Rboolean Rf_isVectorAtomic(SEXP); // Rf_isVectorAtomic used 13 times in agop, tidyr, reshape2, stringi // isVectorAtomic used 40 times in bit, matrixStats, checkmate, PythonInR, data.table, Matrix, bit64, potts, aster2, qtbase Rboolean Rf_isVectorList(SEXP); // Rf_isVectorList used 23 times in genie, purrr, RNiftyReg, stringi // isVectorList used 12 times in RPostgreSQL, spsurvey, PythonInR, stringi, adaptivetau, PCICt, RandomFields Rboolean Rf_isVectorizable(SEXP); // Rf_isVectorizable unused // isVectorizable used 3 times in PythonInR, robfilter SEXP Rf_lang1(SEXP); // Rf_lang1 used 14 times in PopGenome, WhopGenome, nontarget, Rcpp11, purrr, Rcpp, CEGO // lang1 used 30 times in 11 packages SEXP Rf_lang2(SEXP, SEXP); // Rf_lang2 used 64 times in 13 packages // lang2 used 216 times in 75 packages SEXP Rf_lang3(SEXP, SEXP, SEXP); // Rf_lang3 used 19 times in purrr, RcppDE, Rcpp, lbfgs, emdist, Rcpp11 // lang3 used 107 times in 28 packages SEXP Rf_lang4(SEXP, SEXP, SEXP, SEXP); // Rf_lang4 used 8 times in lme4, purrr, Rcpp, diversitree, Rcpp11 // lang4 used 65 times in 21 packages SEXP Rf_lang5(SEXP, SEXP, SEXP, SEXP, SEXP); // Rf_lang5 unused // lang5 used 11 times in PBSddesolve, GNE, SMC SEXP Rf_lang6(SEXP, SEXP, SEXP, SEXP, SEXP, SEXP); // Rf_lang6 used 1 times in lme4 // lang6 used 2 times in GNE SEXP Rf_lastElt(SEXP); // Rf_lastElt unused // lastElt unused SEXP Rf_lcons(SEXP, SEXP); // Rf_lcons used 26 times in purrr, rcppbugs, Rcpp, pryr // lcons used 16 times in rmgarch R_len_t Rf_length(SEXP); // Rf_length used 662 times in 69 packages SEXP Rf_list1(SEXP); // Rf_list1 used 1 times in Rcpp // list1 used 197 times in 11 packages SEXP Rf_list2(SEXP, SEXP); // Rf_list2 unused // list2 used 441 times in 12 packages SEXP Rf_list3(SEXP, SEXP, SEXP); // Rf_list3 unused // list3 used 72 times in marked, Rdsdp, BH, svd SEXP Rf_list4(SEXP, SEXP, SEXP, SEXP); // Rf_list4 unused // list4 used 58 times in igraph, PBSddesolve, Rserve, BH, yaml, treethresh, SMC SEXP Rf_list5(SEXP, SEXP, SEXP, SEXP, SEXP); // Rf_list5 unused // list5 used 63 times in Rdsdp, BH SEXP Rf_listAppend(SEXP, SEXP); // Rf_listAppend unused // listAppend used 1 times in ore SEXP Rf_mkNamed(SEXPTYPE, const char **); // Rf_mkNamed used 8 times in Matrix, gmp, RSclient, HiPLARM // mkNamed used 12 times in RCassandra, coxme, SamplerCompare, survival, JavaGD, DEoptim, qtbase SEXP Rf_mkString(const char *); // Rf_mkString used 179 times in 24 packages // mkString used 814 times in 96 packages int Rf_nlevels(SEXP); // Rf_nlevels unused // nlevels used 546 times in 26 packages int Rf_stringPositionTr(SEXP, const char *); // Rf_stringPositionTr unused // stringPositionTr unused SEXP Rf_ScalarComplex(Rcomplex); // Rf_ScalarComplex unused // ScalarComplex unused SEXP Rf_ScalarInteger(int); // Rf_ScalarInteger used 390 times in 20 packages // ScalarInteger used 704 times in 88 packages SEXP Rf_ScalarLogical(int); // Rf_ScalarLogical used 160 times in 20 packages // ScalarLogical used 450 times in 64 packages SEXP Rf_ScalarRaw(Rbyte); // Rf_ScalarRaw unused // ScalarRaw used 4 times in qtbase, RGtk2 SEXP Rf_ScalarReal(double); // Rf_ScalarReal used 146 times in 19 packages // ScalarReal used 330 times in 65 packages SEXP Rf_ScalarString(SEXP); // Rf_ScalarString used 33 times in agop, Nippon, Rcpp11, rpf, stringi, purrr, Rcpp // ScalarString used 198 times in 37 packages R_xlen_t Rf_xlength(SEXP); // Rf_xlength used 46 times in WGCNA, Rcpp, Rcpp11 SEXP Rf_protect(SEXP); // Rf_protect used 332 times in 12 packages // protect used 599 times in 101 packages void Rf_unprotect(int); // Rf_unprotect used 289 times in 12 packages // unprotect used 110 times in 35 packages void R_ProtectWithIndex(SEXP, PROTECT_INDEX *); // R_ProtectWithIndex used 8 times in OpenMx void R_Reprotect(SEXP, PROTECT_INDEX); // R_Reprotect used 2 times in OpenMx SEXP R_FixupRHS(SEXP x, SEXP y); // R_FixupRHS unused } # /Users/ls/Source/git/fastr/com.oracle.truffle.r.native/include/Rmath.h extern "C" { double R_pow(double x, double y); // R_pow used 1521 times in 72 packages double R_pow_di(double, int); // R_pow_di used 384 times in 53 packages double norm_rand(void); // norm_rand used 408 times in 93 packages double unif_rand(void); // unif_rand used 2135 times in 327 packages double exp_rand(void); // exp_rand used 110 times in 25 packages double Rf_dnorm4(double, double, double, int); // Rf_dnorm4 used 45 times in 13 packages // dnorm4 used 27 times in 11 packages // dnorm used 1377 times in 151 packages double Rf_pnorm5(double, double, double, int, int); // Rf_pnorm5 used 143 times in 19 packages // pnorm used 1582 times in 159 packages // pnorm5 used 77 times in 12 packages double Rf_qnorm5(double, double, double, int, int); // Rf_qnorm5 used 40 times in 13 packages // qnorm5 used 30 times in igraph, PwrGSD, geepack, robustvarComp, Rcpp11, tpr, Rcpp // qnorm used 444 times in 96 packages double Rf_rnorm(double, double); // Rf_rnorm used 85 times in 13 packages // rnorm used 1865 times in 198 packages void Rf_pnorm_both(double, double *, double *, int, int); // Rf_pnorm_both used 4 times in Rcpp, Rcpp11 // pnorm_both used 12 times in MCMCpack, MasterBayes, Rcpp, phcfM, gof, Rcpp11 double Rf_dunif(double, double, double, int); // Rf_dunif used 4 times in Rcpp, Rcpp11 // dunif used 120 times in 18 packages double Rf_punif(double, double, double, int, int); // Rf_punif used 4 times in Rcpp, Rcpp11 // punif used 70 times in 11 packages double Rf_qunif(double, double, double, int, int); // Rf_qunif used 3 times in Rcpp, Rcpp11 // qunif used 14 times in RInside, qrjoint, Rcpp, Rcpp11, littler double Rf_runif(double, double); // Rf_runif used 112 times in 19 packages // runif used 2810 times in 273 packages double Rf_dgamma(double, double, double, int); // Rf_dgamma used 13 times in lme4, epinet, Rcpp, rtkpp, rtkore, Rcpp11 // dgamma used 617 times in 57 packages double Rf_pgamma(double, double, double, int, int); // Rf_pgamma used 31 times in TMB, Rcpp, rtkpp, BayesFactor, rtkore, Rcpp11 // pgamma used 164 times in 40 packages double Rf_qgamma(double, double, double, int, int); // Rf_qgamma used 12 times in TMB, Rcpp, rtkpp, BayesFactor, rtkore, Rcpp11 // qgamma used 58 times in 25 packages double Rf_rgamma(double, double); // Rf_rgamma used 88 times in 14 packages // rgamma used 786 times in 104 packages double Rf_log1pmx(double); // Rf_log1pmx used 2 times in Rcpp, Rcpp11 // log1pmx used 20 times in DPpackage, BH, Rcpp, Rcpp11 double log1pexp(double); // log1pexp used 4 times in Rcpp, Rcpp11 double Rf_lgamma1p(double); // Rf_lgamma1p used 3 times in OpenMx, Rcpp, Rcpp11 // lgamma1p used 14 times in Rcpp, OpenMx, ergm.count, heavy, mixAK, Rcpp11 double Rf_logspace_add(double, double); // Rf_logspace_add used 2 times in Rcpp, Rcpp11 // logspace_add used 21 times in sna, BMN, Rcpp11, RxCEcolInf, SamplerCompare, STAR, Rcpp double Rf_logspace_sub(double, double); // Rf_logspace_sub used 2 times in Rcpp, Rcpp11 // logspace_sub used 16 times in sna, Rcpp11, SamplerCompare, truncnorm, STAR, Rcpp, bfp double logspace_sum(double *, int); // logspace_sum unused double Rf_dbeta(double, double, double, int); // Rf_dbeta used 14 times in Rcpp, OpenMx, rtkpp, SBSA, rtkore, Rcpp11 // dbeta used 377 times in 54 packages double Rf_pbeta(double, double, double, int, int); // Rf_pbeta used 24 times in Rcpp, bcp, OpenMx, rtkpp, rtkore, Rcpp11 // pbeta used 262 times in 39 packages double Rf_qbeta(double, double, double, int, int); // Rf_qbeta used 7 times in Rcpp, rtkpp, rtkore, Rcpp11 // qbeta used 57 times in 17 packages double Rf_rbeta(double, double); // Rf_rbeta used 14 times in bfa, spBayesSurv, RcppSMC, Rcpp11, Rcpp, rtkpp, rtkore // rbeta used 431 times in 59 packages double Rf_dlnorm(double, double, double, int); // Rf_dlnorm used 13 times in Rcpp, rtkpp, RcppProgress, rtkore, Rcpp11 // dlnorm used 68 times in 22 packages double Rf_plnorm(double, double, double, int, int); // Rf_plnorm used 7 times in Rcpp, rtkpp, rtkore, Rcpp11 // plnorm used 37 times in 14 packages double Rf_qlnorm(double, double, double, int, int); // Rf_qlnorm used 7 times in Rcpp, rtkpp, rtkore, Rcpp11 // qlnorm used 11 times in icenReg, RInside, Rcpp, Rcpp11, littler double Rf_rlnorm(double, double); // Rf_rlnorm used 7 times in Rcpp, rtkpp, RcppSMC, rtkore, Rcpp11 // rlnorm used 64 times in 18 packages double Rf_dchisq(double, double, int); // Rf_dchisq used 11 times in Rcpp, rtkpp, rtkore, Rcpp11 // dchisq used 57 times in 14 packages double Rf_pchisq(double, double, int, int); // Rf_pchisq used 9 times in Rcpp, rtkpp, rtkore, lm.br, Rcpp11 // pchisq used 152 times in 33 packages double Rf_qchisq(double, double, int, int); // Rf_qchisq used 13 times in robustHD, ccaPP, lm.br, Rcpp11, Rcpp, rtkpp, rtkore // qchisq used 38 times in 21 packages double Rf_rchisq(double); // Rf_rchisq used 14 times in bfa, MixedDataImpute, rmgarch, Rcpp11, lme4, Rcpp, rtkpp, rtkore // rchisq used 244 times in 54 packages double Rf_dnchisq(double, double, double, int); // Rf_dnchisq used 3 times in Rcpp, Rcpp11 // dnchisq used 7 times in spc, Rcpp, Rcpp11 double Rf_pnchisq(double, double, double, int, int); // Rf_pnchisq used 3 times in Rcpp, Rcpp11 // pnchisq used 13 times in spc, Rcpp, Rcpp11 double Rf_qnchisq(double, double, double, int, int); // Rf_qnchisq used 3 times in Rcpp, Rcpp11 // qnchisq used 9 times in spc, Rcpp, Rcpp11 double Rf_rnchisq(double, double); // Rf_rnchisq used 2 times in Rcpp, Rcpp11 // rnchisq used 11 times in Rcpp, Rcpp11 double Rf_df(double, double, double, int); // Rf_df used 12 times in Rcpp, subplex, rtkpp, rtkore, Rcpp11 // df unused double Rf_pf(double, double, double, int, int); // Rf_pf used 13 times in BIFIEsurvey, Rcpp, rtkpp, rtkore, lm.br, Rcpp11 // pf unused double Rf_qf(double, double, double, int, int); // Rf_qf used 9 times in Rcpp, rtkpp, rtkore, lm.br, Rcpp11 // qf unused double Rf_rf(double, double); // Rf_rf used 6 times in Rcpp, rtkpp, rtkore, Rcpp11 // rf unused double Rf_dt(double, double, int); // Rf_dt used 12 times in TMB, Rcpp, rtkpp, rtkore, Rcpp11 // dt unused double Rf_pt(double, double, int, int); // Rf_pt used 8 times in Rcpp, rtkpp, rtkore, lm.br, Rcpp11 // pt unused double Rf_qt(double, double, int, int); // Rf_qt used 7 times in Rcpp, rtkpp, rtkore, Rcpp11 // qt unused double Rf_rt(double); // Rf_rt used 7 times in Rcpp, rtkpp, RcppSMC, rtkore, Rcpp11 // rt unused double Rf_dbinom_raw(double x, double n, double p, double q, int give_log); // Rf_dbinom_raw unused // dbinom_raw used 50 times in igraph, MCMCpack, secr, AdaptFitOS, phcfM, gof, MasterBayes, locfit double Rf_dbinom(double, double, double, int); // Rf_dbinom used 23 times in mvabund, Rcpp11, rgam, lme4, unmarked, Rcpp, rtkpp, BayesFactor, rtkore // dbinom used 290 times in 40 packages double Rf_pbinom(double, double, double, int, int); // Rf_pbinom used 10 times in Rcpp, rtkpp, mvabund, rtkore, Rcpp11, rgam // pbinom used 53 times in 16 packages double Rf_qbinom(double, double, double, int, int); // Rf_qbinom used 9 times in Rcpp, rtkpp, mvabund, rtkore, Rcpp11 // qbinom used 18 times in DPpackage, Runuran, BayesXsrc, mvabund, Rcpp11, RInside, Rcpp, ump, littler double Rf_rbinom(double, double); // Rf_rbinom used 14 times in igraph, mvabund, Rcpp11, Rcpp, rtkpp, rtkore, RcppArmadillo // rbinom used 169 times in 50 packages void Rf_rmultinom(int, double*, int, int*); // Rf_rmultinom unused // rmultinom used 42 times in 18 packages double Rf_dcauchy(double, double, double, int); // Rf_dcauchy used 15 times in lme4, Rcpp, rtkpp, BayesFactor, rtkore, Rcpp11 // dcauchy used 25 times in DPpackage, multimark, vcrpart, kernlab, Rcpp11, RInside, Rcpp, aucm, ordinal, littler double Rf_pcauchy(double, double, double, int, int); // Rf_pcauchy used 10 times in lme4, Rcpp, rtkpp, rtkore, Rcpp11 // pcauchy used 25 times in DPpackage, vcrpart, Rcpp11, RInside, Rcpp, ordinal, RandomFields, littler double Rf_qcauchy(double, double, double, int, int); // Rf_qcauchy used 10 times in lme4, Rcpp, rtkpp, rtkore, Rcpp11 // qcauchy used 11 times in RInside, DPpackage, Rcpp, Rcpp11, littler double Rf_rcauchy(double, double); // Rf_rcauchy used 7 times in Rcpp, rtkpp, RcppSMC, rtkore, Rcpp11 // rcauchy used 21 times in PoweR, RInside, Rcpp, DEoptim, Rcpp11, littler double Rf_dexp(double, double, int); // Rf_dexp used 12 times in unmarked, Rcpp, rtkpp, rtkore, Rcpp11 // dexp used 646 times in 82 packages double Rf_pexp(double, double, int, int); // Rf_pexp used 11 times in unmarked, Rcpp, rtkpp, BayesFactor, rtkore, Rcpp11 // pexp used 117 times in 26 packages double Rf_qexp(double, double, int, int); // Rf_qexp used 7 times in Rcpp, rtkpp, rtkore, Rcpp11 // qexp used 20 times in monomvn, GeoGenetix, Rcpp11, icenReg, RInside, TMB, Rcpp, Sunder, RandomFields, littler double Rf_rexp(double); // Rf_rexp used 20 times in iBATCGH, RcppSMC, rmgarch, Rcpp11, wrswoR, Rcpp, rtkpp, BayesFactor, rtkore // rexp used 224 times in 56 packages double Rf_dgeom(double, double, int); // Rf_dgeom used 11 times in Rcpp, rtkpp, rtkore, Rcpp11 // dgeom used 16 times in RInside, Rcpp, ergm.count, Rcpp11, littler double Rf_pgeom(double, double, int, int); // Rf_pgeom used 7 times in Rcpp, rtkpp, rtkore, Rcpp11 // pgeom used 10 times in RInside, Rcpp, Rcpp11, littler double Rf_qgeom(double, double, int, int); // Rf_qgeom used 7 times in Rcpp, rtkpp, rtkore, Rcpp11 // qgeom used 10 times in RInside, Rcpp, Rcpp11, littler double Rf_rgeom(double); // Rf_rgeom used 13 times in igraph, Rcpp, iBATCGH, rtkpp, rtkore, Rcpp11 // rgeom used 25 times in BSquare, sna, ergm.count, Rcpp11, RInside, Rcpp, littler double Rf_dhyper(double, double, double, double, int); // Rf_dhyper used 11 times in Rcpp, rtkpp, rtkore, Rcpp11 // dhyper used 14 times in AdaptFitOS, Rcpp11, RInside, Rcpp, CorrBin, locfit, littler double Rf_phyper(double, double, double, double, int, int); // Rf_phyper used 7 times in Rcpp, rtkpp, rtkore, Rcpp11 // phyper used 17 times in Runuran, Rcpp11, cpm, RInside, Rcpp, RandomFields, vegan, littler double Rf_qhyper(double, double, double, double, int, int); // Rf_qhyper used 7 times in Rcpp, rtkpp, rtkore, Rcpp11 // qhyper used 11 times in RInside, Runuran, Rcpp, Rcpp11, littler double Rf_rhyper(double, double, double); // Rf_rhyper used 8 times in Rcpp, rtkpp, rtkore, Rcpp11 // rhyper used 13 times in kSamples, RInside, Rcpp, Rcpp11, littler double Rf_dnbinom(double, double, double, int); // Rf_dnbinom used 22 times in inarmix, Rcpp, rtkpp, mvabund, rtkore, Rcpp11 // dnbinom used 170 times in 27 packages double Rf_pnbinom(double, double, double, int, int); // Rf_pnbinom used 10 times in Rcpp, rtkpp, mvabund, rtkore, Rcpp11 // pnbinom used 29 times in 13 packages double Rf_qnbinom(double, double, double, int, int); // Rf_qnbinom used 10 times in Rcpp, rtkpp, mvabund, rtkore, Rcpp11 // qnbinom used 12 times in RInside, Runuran, Rcpp, mvabund, Rcpp11, littler double Rf_rnbinom(double, double); // Rf_rnbinom used 9 times in Rcpp, rtkpp, mvabund, rtkore, Rcpp11 // rnbinom used 41 times in 18 packages double Rf_dnbinom_mu(double, double, double, int); // Rf_dnbinom_mu used 1 times in Rcpp // dnbinom_mu used 18 times in RDS, KFAS, Rcpp11, unmarked, Rcpp, sspse, Bclim double Rf_pnbinom_mu(double, double, double, int, int); // Rf_pnbinom_mu used 1 times in Rcpp // pnbinom_mu used 3 times in Rcpp, Rcpp11 double Rf_qnbinom_mu(double, double, double, int, int); // Rf_qnbinom_mu used 1 times in Rcpp // qnbinom_mu used 3 times in Rcpp, Rcpp11 double Rf_rnbinom_mu(double, double); // Rf_rnbinom_mu used 1 times in Rcpp // rnbinom_mu used 7 times in Rcpp, Rcpp11 double Rf_dpois_raw (double, double, int); // Rf_dpois_raw unused // dpois_raw used 25 times in igraph, MCMCpack, AdaptFitOS, phcfM, gof, MasterBayes, locfit double Rf_dpois(double, double, int); // Rf_dpois used 28 times in mvabund, Rcpp11, rgam, lme4, unmarked, Rcpp, rtkpp, rtkore // dpois used 212 times in 37 packages double Rf_ppois(double, double, int, int); // Rf_ppois used 13 times in mvabund, Rcpp11, rgam, TMB, Rcpp, rtkpp, rtkore // ppois used 62 times in 18 packages double Rf_qpois(double, double, int, int); // Rf_qpois used 10 times in Rcpp, rtkpp, mvabund, rtkore, Rcpp11 // qpois used 23 times in 11 packages double Rf_rpois(double); // Rf_rpois used 22 times in mvabund, Rcpp11, Rcpp, RcppOctave, fwsim, rtkpp, rtkore // rpois used 157 times in 51 packages double Rf_dweibull(double, double, double, int); // Rf_dweibull used 11 times in Rcpp, rtkpp, rtkore, Rcpp11 // dweibull used 38 times in 16 packages double Rf_pweibull(double, double, double, int, int); // Rf_pweibull used 7 times in Rcpp, rtkpp, rtkore, Rcpp11 // pweibull used 42 times in 14 packages double Rf_qweibull(double, double, double, int, int); // Rf_qweibull used 7 times in Rcpp, rtkpp, rtkore, Rcpp11 // qweibull used 16 times in BSquare, Rcpp11, icenReg, RInside, TMB, extWeibQuant, Rcpp, littler double Rf_rweibull(double, double); // Rf_rweibull used 6 times in Rcpp, rtkpp, rtkore, Rcpp11 // rweibull used 35 times in 12 packages double Rf_dlogis(double, double, double, int); // Rf_dlogis used 14 times in lme4, Rcpp, rtkpp, BayesFactor, rtkore, Rcpp11 // dlogis used 91 times in 18 packages double Rf_plogis(double, double, double, int, int); // Rf_plogis used 8 times in lme4, Rcpp, rtkpp, rtkore, Rcpp11 // plogis used 125 times in 21 packages double Rf_qlogis(double, double, double, int, int); // Rf_qlogis used 9 times in lme4, Rcpp, rtkpp, BayesFactor, rtkore, Rcpp11 // qlogis used 16 times in DPpackage, geoBayes, Rcpp11, RInside, TMB, qrjoint, Rcpp, littler double Rf_rlogis(double, double); // Rf_rlogis used 10 times in Rcpp, rtkpp, BayesFactor, rtkore, Rcpp11 // rlogis used 32 times in MCMCpack, phcfM, gof, Rcpp11, MasterBayes, PoweR, RInside, Rcpp, littler double Rf_dnbeta(double, double, double, double, int); // Rf_dnbeta used 4 times in OpenMx, Rcpp, Rcpp11 // dnbeta used 6 times in Rcpp, Rcpp11 double Rf_pnbeta(double, double, double, double, int, int); // Rf_pnbeta used 4 times in OpenMx, Rcpp, Rcpp11 // pnbeta used 23 times in bayesSurv, Rcpp, Rcpp11 double Rf_qnbeta(double, double, double, double, int, int); // Rf_qnbeta used 3 times in Rcpp, Rcpp11 // qnbeta used 8 times in Rcpp, Rcpp11 double Rf_rnbeta(double, double, double); // Rf_rnbeta used 2 times in Rcpp, Rcpp11 // rnbeta used 4 times in Rcpp, Rcpp11 double Rf_dnf(double, double, double, double, int); // Rf_dnf used 3 times in Rcpp, Rcpp11 // dnf used 13 times in RxODE, Rcpp, Rcpp11 double Rf_pnf(double, double, double, double, int, int); // Rf_pnf used 3 times in Rcpp, Rcpp11 // pnf used 12 times in Rcpp, Rcpp11 double Rf_qnf(double, double, double, double, int, int); // Rf_qnf used 3 times in Rcpp, Rcpp11 // qnf used 8 times in Rcpp, Rcpp11 double Rf_dnt(double, double, double, int); // Rf_dnt used 4 times in BayesFactor, Rcpp, Rcpp11 // dnt used 17 times in alineR, DNAtools, gmum.r, Rcpp11, Rcpp, bayesLife, spc double Rf_pnt(double, double, double, int, int); // Rf_pnt used 3 times in Rcpp, Rcpp11 // pnt used 111 times in BayesXsrc, hypervolume, Rcpp, spc, Rcpp11 double Rf_qnt(double, double, double, int, int); // Rf_qnt used 3 times in Rcpp, Rcpp11 // qnt used 12 times in ore, Rcpp, spc, Rcpp11 double Rf_ptukey(double, double, double, double, int, int); // Rf_ptukey used 2 times in Rcpp, Rcpp11 // ptukey used 6 times in RInside, Rcpp, Rcpp11, littler double Rf_qtukey(double, double, double, double, int, int); // Rf_qtukey used 2 times in Rcpp, Rcpp11 // qtukey used 6 times in RInside, Rcpp, Rcpp11, littler double Rf_dwilcox(double, double, double, int); // Rf_dwilcox used 2 times in Rcpp, Rcpp11 // dwilcox used 12 times in clinfun, fuzzyRankTests, Rcpp11, RInside, Rcpp, DescTools, littler double Rf_pwilcox(double, double, double, int, int); // Rf_pwilcox used 2 times in Rcpp, Rcpp11 // pwilcox used 16 times in fuzzyRankTests, Rcpp11, FRESA.CAD, RInside, simctest, Rcpp, littler double Rf_qwilcox(double, double, double, int, int); // Rf_qwilcox used 2 times in Rcpp, Rcpp11 // qwilcox used 10 times in RInside, Rcpp, Rcpp11, littler double Rf_rwilcox(double, double); // Rf_rwilcox used 4 times in Rcpp, Rcpp11 // rwilcox used 11 times in RInside, Rcpp, Rcpp11, littler double Rf_dsignrank(double, double, int); // Rf_dsignrank used 2 times in Rcpp, Rcpp11 // dsignrank used 7 times in RInside, Rcpp, fuzzyRankTests, Rcpp11, littler double Rf_psignrank(double, double, int, int); // Rf_psignrank used 2 times in Rcpp, Rcpp11 // psignrank used 11 times in FRESA.CAD, RInside, Rcpp, fuzzyRankTests, Rcpp11, littler double Rf_qsignrank(double, double, int, int); // Rf_qsignrank used 2 times in Rcpp, Rcpp11 // qsignrank used 6 times in RInside, Rcpp, Rcpp11, littler double Rf_rsignrank(double); // Rf_rsignrank used 4 times in Rcpp, Rcpp11 // rsignrank used 11 times in RInside, Rcpp, Rcpp11, littler double Rf_gammafn(double); // Rf_gammafn used 7 times in Rcpp, Rcpp11 // gammafn used 374 times in 46 packages double Rf_lgammafn(double); // Rf_lgammafn used 61 times in epinet, spBayesSurv, AdaptFitOS, rmgarch, Rcpp11, icenReg, TMB, Rcpp, locfit, OpenMx // lgammafn used 407 times in 66 packages double Rf_lgammafn_sign(double, int*); // Rf_lgammafn_sign used 2 times in Rcpp, Rcpp11 // lgammafn_sign used 4 times in Rcpp, Rcpp11 void Rf_dpsifn(double, int, int, int, double*, int*, int*); // Rf_dpsifn used 2 times in Rcpp, Rcpp11 // dpsifn used 4 times in Rcpp, Rcpp11 double Rf_psigamma(double, double); // Rf_psigamma used 6 times in TMB, Rcpp, Rcpp11 // psigamma used 9 times in Rcpp, Rcpp11 double Rf_digamma(double); // Rf_digamma used 47 times in inarmix, stochvol, Rcpp, frailtySurv, Rcpp11 // digamma used 20689 times in 54 packages double Rf_trigamma(double); // Rf_trigamma used 10 times in stochvol, Rcpp, frailtySurv, Rcpp11 // trigamma used 128 times in 24 packages double Rf_tetragamma(double); // Rf_tetragamma used 5 times in Rcpp, Rcpp11 // tetragamma used 22 times in Rcpp, Rcpp11, RcppShark double Rf_pentagamma(double); // Rf_pentagamma used 5 times in Rcpp, Rcpp11 // pentagamma used 8 times in Rcpp, Rcpp11 double Rf_beta(double, double); // Rf_beta used 8 times in Rcpp, iBATCGH, RandomFields, Rcpp11 // beta used 32773 times in 615 packages double Rf_lbeta(double, double); // Rf_lbeta used 24 times in Rcpp, poisDoubleSamp, bcp, Rcpp11 // lbeta used 213 times in 23 packages double Rf_choose(double, double); // Rf_choose used 9 times in DepthProc, Rcpp, bfp, polyfreqs, Rcpp11 // choose used 1368 times in 287 packages double Rf_lchoose(double, double); // Rf_lchoose used 38 times in Rcpp, bfp, poisDoubleSamp, noncompliance, Rcpp11 // lchoose used 54 times in 17 packages double Rf_bessel_i(double, double, double); // Rf_bessel_i used 3 times in OpenMx, Rcpp, Rcpp11 // bessel_i used 29 times in BiTrinA, Binarize, overlap, RCALI, Hankel, Rcpp11, rotations, Rcpp, moveHMM, dti double Rf_bessel_j(double, double); // Rf_bessel_j used 3 times in OpenMx, Rcpp, Rcpp11 // bessel_j used 25 times in SpatialExtremes, constrainedKriging, BH, Rcpp, RandomFields, Rcpp11 double Rf_bessel_k(double, double, double); // Rf_bessel_k used 7 times in TMB, Rcpp, OpenMx, rmgarch, Rcpp11 // bessel_k used 127 times in 26 packages double Rf_bessel_y(double, double); // Rf_bessel_y used 3 times in OpenMx, Rcpp, Rcpp11 // bessel_y used 4 times in Rcpp, Rcpp11 double Rf_bessel_i_ex(double, double, double, double *); // Rf_bessel_i_ex used 2 times in Rcpp, Rcpp11 // bessel_i_ex used 5 times in Rcpp, Rcpp11, dti double Rf_bessel_j_ex(double, double, double *); // Rf_bessel_j_ex used 2 times in Rcpp, Rcpp11 // bessel_j_ex used 4 times in Rcpp, Rcpp11 double Rf_bessel_k_ex(double, double, double, double *); // Rf_bessel_k_ex used 2 times in Rcpp, Rcpp11 // bessel_k_ex used 9 times in geostatsp, Rcpp, tgp, Rcpp11 double Rf_bessel_y_ex(double, double, double *); // Rf_bessel_y_ex used 2 times in Rcpp, Rcpp11 // bessel_y_ex used 4 times in Rcpp, Rcpp11 double Rf_pythag(double, double); // Rf_pythag used 4 times in Rcpp, Rcpp11 // pythag used 105 times in 21 packages int Rf_imax2(int, int); // Rf_imax2 used 2 times in Rcpp, Rcpp11 // imax2 used 150 times in 37 packages int Rf_imin2(int, int); // Rf_imin2 used 2 times in Rcpp, Rcpp11 // imin2 used 193 times in 28 packages double Rf_fmax2(double, double); // Rf_fmax2 used 2 times in Rcpp, Rcpp11 // fmax2 used 345 times in 60 packages double Rf_fmin2(double, double); // Rf_fmin2 used 4 times in TMB, Rcpp, Rcpp11 // fmin2 used 224 times in 46 packages double Rf_sign(double); // Rf_sign used 4 times in OpenMx, Rcpp, Rcpp11 // sign used 5291 times in 389 packages double Rf_fprec(double, double); // Rf_fprec used 4 times in Rcpp, Rcpp11 // fprec used 38 times in wfe, Rcpp, msm, list, Rcpp11 double Rf_fround(double, double); // Rf_fround used 8 times in Rcpp, RcppClassic, Rcpp11 // fround used 13 times in bioPN, exactLoglinTest, frontiles, Rcpp11, FRESA.CAD, Rcpp, rmetasim, treethresh double Rf_fsign(double, double); // Rf_fsign used 2 times in Rcpp, Rcpp11 // fsign used 66 times in 15 packages double Rf_ftrunc(double); // Rf_ftrunc used 4 times in Rcpp, Rcpp11 // ftrunc used 123 times in 22 packages double Rf_log1pmx(double); // Rf_log1pmx used 2 times in Rcpp, Rcpp11 // log1pmx used 20 times in DPpackage, BH, Rcpp, Rcpp11 double Rf_lgamma1p(double); // Rf_lgamma1p used 3 times in OpenMx, Rcpp, Rcpp11 // lgamma1p used 14 times in Rcpp, OpenMx, ergm.count, heavy, mixAK, Rcpp11 double cospi(double); // cospi used 1 times in Rmpfr double sinpi(double); // sinpi used 1 times in Rmpfr double tanpi(double); // tanpi used 1 times in Rmpfr double Rf_logspace_add(double logx, double logy); // Rf_logspace_add used 2 times in Rcpp, Rcpp11 // logspace_add used 21 times in sna, BMN, Rcpp11, RxCEcolInf, SamplerCompare, STAR, Rcpp double Rf_logspace_sub(double logx, double logy); // Rf_logspace_sub used 2 times in Rcpp, Rcpp11 // logspace_sub used 16 times in sna, Rcpp11, SamplerCompare, truncnorm, STAR, Rcpp, bfp } # /Users/ls/Source/git/fastr/com.oracle.truffle.r.native/include/S.h extern "C" { extern void seed_in(long *); // seed_in used 11 times in raster, excursions, IGM.MEA, GENLIB, VLMC, maptools, robust extern void seed_out(long *); // seed_out used 7 times in GENLIB, raster, VLMC, maptools, robust, IGM.MEA extern double unif_rand(void); // unif_rand used 2135 times in 327 packages extern double norm_rand(void); // norm_rand used 408 times in 93 packages typedef struct { double re; double im; } S_complex; // S_complex used 2 times in ifultools } </pre> == Stats == <pre> 0 1 2 3 4 5 6 7 8 9 10+ Macro: 129 12 15 12 12 4 8 3 0 7 208 (usage count) (410) 129 34 20 12 22 12 9 11 5 6 150 (distinct package count) Function: 259 32 35 25 33 15 18 16 11 9 351 (usage count) (804) 259 65 50 41 48 27 29 13 9 21 242 (distinct package count) Variable: 32 2 6 4 2 1 2 1 0 0 22 (usage count) (72) 32 8 5 3 1 1 1 2 0 1 18 (distinct package count) TypeDef: 10 0 1 2 0 0 0 2 2 0 13 (usage count) (30) 10 1 2 2 0 1 0 2 2 0 10 (distinct package count) Alias: 68 14 26 14 13 8 6 6 4 4 213 (usage count) (376) 68 42 41 18 20 10 12 16 9 3 137 (distinct package count) </pre> (for a quick explanation of these stats see [[Native_API_stats_of_R.h]]) 082666a1c60259540269f0ee8b24ed93fa7a73a3 0 16 71 69 2017-03-17T16:01:48Z Jpmurillo 17 wikitext text/x-wiki == Background == We have seen an exponential increase in the demand of R among a large and variant set of audiences. People from various domains are keen to learn it and further improve their skills. This has created a supply and demand gap which is being filled by various teaching channels. While there is no dearth of the amount of R teaching material, both in-class and online, there is still arguably a shortage in skilled R users who possess quality skills in R. This shortage of qualified personnel and abundance of self-taught data scientists leads to confusion for employers as well as prospective employees who have the required skill-set but no way to differentiate themselves. == Proposal == There is no system today to certify qualified R Professionals. The R-Consortium, as the governing body for the R community, needs to step in as the neutral agency before another third-party comes in with a similar certification mechanism and tries to fill this gap. From a competitive perspective, SPSS and SAS already has a certification mechanism in place. == Moving Parts == We understand that there are multiple moving pieces and we have identified 3 main areas to bucket them – 1. Specialization 2. Training 3. Testing 4. Certification We have also taken a stab at the sub categories under those buckets and the multiple paths that we can have for those functional areas that would allow the R Community to solve for the above mentioned challenges. [[File:Initial Path Visual.PNG|thumb|The likely path of certification to be taken in the initial cut]] == Key decisions to be made == The WG will work on the following elements regarding R certification: 1. Financials – * Cost ** Vendor Cost ** Consortium Cost ** Student Cost * Profits 2. Marketing and Promotions 3. Testing * Pass/Fail outcome * Percentage – 0 to 100% * Certification Levels 4. How long the certificate remains valid 5. Partnerships * Training * Testing * Certifications 6. Curriculum – * Generic * Pharmacy * Financial * Visualizations * Others 7. Phased Approach * Identify the next couple of phases * Phase 1 * Phase 2 == Members == * David Smith (Microsoft) * Jeremy Reynolds (Microsoft) * Jonathan Cornelissen (Datacamp) * Martijn Theuwissen (DataCamp) * Mark Sellors (Mango Solution) * Aimee Gott (Mango Solutions) * ''Hadley Wickham (ISC liason, RStudio)'' * Mine Cetinkaya-Rundel (RStudio) * Dinesh Nirmal (IBM) * Trishan de Lanerolle (Linux Foundation) * '''MeharPratap Singh (ProCogia)''' * JuanPablo Murillo (ProCogia) == Milestones == === Achieved === == Open Questions == == Minutes == ==== 2/1 Working Group Meeting ==== Attendees: Mine Cetinkaya-Rundel, Trishan de Lanerolle, Mehar Singh, JuanPablo Murillo, Mark Sellors * Identified tech representatives in partner companies to collaborate on domain objective for certification. * Discussed potential focuses of certification and certification seeker’s profile. * Agreed on reaching out to connections at Linux Foundation for their expertise in technical certification logistics and setup. * Updated deadline of domain objective completion to 2/28 and deadline of opening domain objective to R community in mid March. ==== 2/13, 2/22, 3/1 Technical Representative Meetings - Domain Objective ==== Attendees: Richie Cotton, Aimee Gott, Garrett Grolemund, Mehar Singh, JuanPablo Murillo, Mine Cetinkaya-Rundel, Jeremy Reynolds, Nick Carchedi * Developed a comprehensive content outline, which breaks R programming proficiency into several competency areas. * Put together a list of packages/libraries to accompany content outline. * Discussed and proposed a composite score in addition to a pass/fail final score. Composite score would take into account mastery within each competency area of the outline. ====3/17 Working Group Meeting==== Attendees: Richie Cotton, Aimee Gott, Mehar Singh, JuanPablo Murillo, Mine Cetinkaya-Rundel, Mark Sellors, Nick Carchedi, Clyde Seepersad, David Smith * Updated larger group of recent progress with regard to certification topics and scoring approach. * Shared completed certification content outline and package list internally with the Working Group. * Discussed financial details of launching a performance based certification with an expert in the field. 706c65edd5539334e6db33264573ede76e22376a 69 68 2017-03-09T17:27:16Z Jpmurillo 17 /* Members */ wikitext text/x-wiki == Background == We have seen an exponential increase in the demand of R among a large and variant set of audiences. People from various domains are keen to learn it and further improve their skills. This has created a supply and demand gap which is being filled by various teaching channels. While there is no dearth of the amount of R teaching material, both in-class and online, there is still arguably a shortage in skilled R users who possess quality skills in R. This shortage of qualified personnel and abundance of self-taught data scientists leads to confusion for employers as well as prospective employees who have the required skill-set but no way to differentiate themselves. == Proposal == There is no system today to certify qualified R Professionals. The R-Consortium, as the governing body for the R community, needs to step in as the neutral agency before another third-party comes in with a similar certification mechanism and tries to fill this gap. From a competitive perspective, SPSS and SAS already has a certification mechanism in place. == Moving Parts == We understand that there are multiple moving pieces and we have identified 3 main areas to bucket them – 1. Specialization 2. Training 3. Testing 4. Certification We have also taken a stab at the sub categories under those buckets and the multiple paths that we can have for those functional areas that would allow the R Community to solve for the above mentioned challenges. [[File:Initial Path Visual.PNG|thumb|The likely path of certification to be taken in the initial cut]] == Key decisions to be made == The WG will work on the following elements regarding R certification: 1. Financials – * Cost ** Vendor Cost ** Consortium Cost ** Student Cost * Profits 2. Marketing and Promotions 3. Testing * Pass/Fail outcome * Percentage – 0 to 100% * Certification Levels 4. How long the certificate remains valid 5. Partnerships * Training * Testing * Certifications 6. Curriculum – * Generic * Pharmacy * Financial * Visualizations * Others 7. Phased Approach * Identify the next couple of phases * Phase 1 * Phase 2 == Members == * David Smith (Microsoft) * Jeremy Reynolds (Microsoft) * Jonathan Cornelissen (Datacamp) * Martijn Theuwissen (DataCamp) * Mark Sellors (Mango Solution) * Aimee Gott (Mango Solutions) * ''Hadley Wickham (ISC liason, RStudio)'' * Mine Cetinkaya-Rundel (RStudio) * Dinesh Nirmal (IBM) * Trishan de Lanerolle (Linux Foundation) * '''MeharPratap Singh (ProCogia)''' * JuanPablo Murillo (ProCogia) == Milestones == === Achieved === == Open Questions == == Minutes == ==== 2/1 Working Group Meeting ==== Attendees: Mine Cetinkaya-Rundel, Trishan de Lanerolle, Mehar Singh, JuanPablo Murillo, Mark Sellors * Identified tech representatives in partner companies to collaborate on domain objective for certification. * Discussed potential focuses of certification and certification seeker’s profile. * Agreed on reaching out to connections at Linux Foundation for their expertise in technical certification logistics and setup. * Updated deadline of domain objective completion to 2/28 and deadline of opening domain objective to R community in mid March. ==== 2/13, 2/22, 3/1 Technical Representative Meetings - Domain Objective ==== Attendees: Richie Cotton, Aimee Gott, Garrett Grolemund, Mehar Singh, JuanPablo Murillo, Mine Cetinkaya-Rundel, Jeremy Reynolds, Nick Carchedi * Developed a comprehensive content outline, which breaks R programming proficiency into several competency areas. * Put together a list of packages/libraries to accompany content outline. * Discussed and proposed a composite score in addition to a pass/fail final score. Composite score would take into account mastery within each competency area of the outline. 2a92de1b15a60e2c3aaf593e0c0d35f2f7aba8ad 68 67 2017-03-09T17:26:40Z Jpmurillo 17 /* Minutes */ wikitext text/x-wiki == Background == We have seen an exponential increase in the demand of R among a large and variant set of audiences. People from various domains are keen to learn it and further improve their skills. This has created a supply and demand gap which is being filled by various teaching channels. While there is no dearth of the amount of R teaching material, both in-class and online, there is still arguably a shortage in skilled R users who possess quality skills in R. This shortage of qualified personnel and abundance of self-taught data scientists leads to confusion for employers as well as prospective employees who have the required skill-set but no way to differentiate themselves. == Proposal == There is no system today to certify qualified R Professionals. The R-Consortium, as the governing body for the R community, needs to step in as the neutral agency before another third-party comes in with a similar certification mechanism and tries to fill this gap. From a competitive perspective, SPSS and SAS already has a certification mechanism in place. == Moving Parts == We understand that there are multiple moving pieces and we have identified 3 main areas to bucket them – 1. Specialization 2. Training 3. Testing 4. Certification We have also taken a stab at the sub categories under those buckets and the multiple paths that we can have for those functional areas that would allow the R Community to solve for the above mentioned challenges. [[File:Initial Path Visual.PNG|thumb|The likely path of certification to be taken in the initial cut]] == Key decisions to be made == The WG will work on the following elements regarding R certification: 1. Financials – * Cost ** Vendor Cost ** Consortium Cost ** Student Cost * Profits 2. Marketing and Promotions 3. Testing * Pass/Fail outcome * Percentage – 0 to 100% * Certification Levels 4. How long the certificate remains valid 5. Partnerships * Training * Testing * Certifications 6. Curriculum – * Generic * Pharmacy * Financial * Visualizations * Others 7. Phased Approach * Identify the next couple of phases * Phase 1 * Phase 2 == Members == * David Smith (Microsoft) * Jeremy Reynolds (Microsoft) * Jonathan Cornelissen (Datacamp) * Martijn Theuwissen (DataCamp) * Mark Sellors (Mango Solution) * Aimee Gott (Mango Solutions) * ''Hadley Wickham (ISC liason, RStudio)'' * Mine Cetinkaya-Rundel (RStudio) * Dinesh Nirmal (IBM) * Trishan de Lanerolle (Linux Foundation) * '''MeharPratap Singh (ProCogia)''' == Milestones == === Achieved === == Open Questions == == Minutes == ==== 2/1 Working Group Meeting ==== Attendees: Mine Cetinkaya-Rundel, Trishan de Lanerolle, Mehar Singh, JuanPablo Murillo, Mark Sellors * Identified tech representatives in partner companies to collaborate on domain objective for certification. * Discussed potential focuses of certification and certification seeker’s profile. * Agreed on reaching out to connections at Linux Foundation for their expertise in technical certification logistics and setup. * Updated deadline of domain objective completion to 2/28 and deadline of opening domain objective to R community in mid March. ==== 2/13, 2/22, 3/1 Technical Representative Meetings - Domain Objective ==== Attendees: Richie Cotton, Aimee Gott, Garrett Grolemund, Mehar Singh, JuanPablo Murillo, Mine Cetinkaya-Rundel, Jeremy Reynolds, Nick Carchedi * Developed a comprehensive content outline, which breaks R programming proficiency into several competency areas. * Put together a list of packages/libraries to accompany content outline. * Discussed and proposed a composite score in addition to a pass/fail final score. Composite score would take into account mastery within each competency area of the outline. 14fafd0eac5f50436b032090f9e2084ab522c2e0 67 66 2017-03-09T17:19:54Z Jpmurillo 17 /* Minutes */ wikitext text/x-wiki == Background == We have seen an exponential increase in the demand of R among a large and variant set of audiences. People from various domains are keen to learn it and further improve their skills. This has created a supply and demand gap which is being filled by various teaching channels. While there is no dearth of the amount of R teaching material, both in-class and online, there is still arguably a shortage in skilled R users who possess quality skills in R. This shortage of qualified personnel and abundance of self-taught data scientists leads to confusion for employers as well as prospective employees who have the required skill-set but no way to differentiate themselves. == Proposal == There is no system today to certify qualified R Professionals. The R-Consortium, as the governing body for the R community, needs to step in as the neutral agency before another third-party comes in with a similar certification mechanism and tries to fill this gap. From a competitive perspective, SPSS and SAS already has a certification mechanism in place. == Moving Parts == We understand that there are multiple moving pieces and we have identified 3 main areas to bucket them – 1. Specialization 2. Training 3. Testing 4. Certification We have also taken a stab at the sub categories under those buckets and the multiple paths that we can have for those functional areas that would allow the R Community to solve for the above mentioned challenges. [[File:Initial Path Visual.PNG|thumb|The likely path of certification to be taken in the initial cut]] == Key decisions to be made == The WG will work on the following elements regarding R certification: 1. Financials – * Cost ** Vendor Cost ** Consortium Cost ** Student Cost * Profits 2. Marketing and Promotions 3. Testing * Pass/Fail outcome * Percentage – 0 to 100% * Certification Levels 4. How long the certificate remains valid 5. Partnerships * Training * Testing * Certifications 6. Curriculum – * Generic * Pharmacy * Financial * Visualizations * Others 7. Phased Approach * Identify the next couple of phases * Phase 1 * Phase 2 == Members == * David Smith (Microsoft) * Jeremy Reynolds (Microsoft) * Jonathan Cornelissen (Datacamp) * Martijn Theuwissen (DataCamp) * Mark Sellors (Mango Solution) * Aimee Gott (Mango Solutions) * ''Hadley Wickham (ISC liason, RStudio)'' * Mine Cetinkaya-Rundel (RStudio) * Dinesh Nirmal (IBM) * Trishan de Lanerolle (Linux Foundation) * '''MeharPratap Singh (ProCogia)''' == Milestones == === Achieved === == Open Questions == == Minutes == 2/1 Working Group Meeting: Attendees: Mine Cetinkaya-Rundel, Trishan de Lanerolle, Mehar Singh, JuanPablo Murillo, Mark Sellors Identified tech representatives in partner companies to collaborate on domain objective for certification. Discussed potential focuses of certification and certification seeker’s profile. Agreed on reaching out to connections at Linux Foundation for their expertise in technical certification logistics and setup. Updated deadline of domain objective completion to 2/28 and deadline of opening domain objective to R community in mid March. 2/13, 2/22, 3/1 Technical Representative Meetings - Domain Objective Attendees: Richie Cotton, Aimee Gott, Garrett Grolemund, Mehar Singh, JuanPablo Murillo, Mine Cetinkaya-Rundel, Jeremy Reynolds, Nick Carchedi Developed a comprehensive content outline, which breaks R programming proficiency into several competency areas. Put together a list of packages/libraries to accompany content outline. Discussed and proposed a composite score in addition to a pass/fail final score. Composite score would take into account mastery within each competency area of the outline. 582fc97f75376443dbeb1fd58d0295d9e1e61817 66 65 2017-01-21T16:08:47Z MeharPratapSingh 15 /* Members */ wikitext text/x-wiki == Background == We have seen an exponential increase in the demand of R among a large and variant set of audiences. People from various domains are keen to learn it and further improve their skills. This has created a supply and demand gap which is being filled by various teaching channels. While there is no dearth of the amount of R teaching material, both in-class and online, there is still arguably a shortage in skilled R users who possess quality skills in R. This shortage of qualified personnel and abundance of self-taught data scientists leads to confusion for employers as well as prospective employees who have the required skill-set but no way to differentiate themselves. == Proposal == There is no system today to certify qualified R Professionals. The R-Consortium, as the governing body for the R community, needs to step in as the neutral agency before another third-party comes in with a similar certification mechanism and tries to fill this gap. From a competitive perspective, SPSS and SAS already has a certification mechanism in place. == Moving Parts == We understand that there are multiple moving pieces and we have identified 3 main areas to bucket them – 1. Specialization 2. Training 3. Testing 4. Certification We have also taken a stab at the sub categories under those buckets and the multiple paths that we can have for those functional areas that would allow the R Community to solve for the above mentioned challenges. [[File:Initial Path Visual.PNG|thumb|The likely path of certification to be taken in the initial cut]] == Key decisions to be made == The WG will work on the following elements regarding R certification: 1. Financials – * Cost ** Vendor Cost ** Consortium Cost ** Student Cost * Profits 2. Marketing and Promotions 3. Testing * Pass/Fail outcome * Percentage – 0 to 100% * Certification Levels 4. How long the certificate remains valid 5. Partnerships * Training * Testing * Certifications 6. Curriculum – * Generic * Pharmacy * Financial * Visualizations * Others 7. Phased Approach * Identify the next couple of phases * Phase 1 * Phase 2 == Members == * David Smith (Microsoft) * Jeremy Reynolds (Microsoft) * Jonathan Cornelissen (Datacamp) * Martijn Theuwissen (DataCamp) * Mark Sellors (Mango Solution) * Aimee Gott (Mango Solutions) * ''Hadley Wickham (ISC liason, RStudio)'' * Mine Cetinkaya-Rundel (RStudio) * Dinesh Nirmal (IBM) * Trishan de Lanerolle (Linux Foundation) * '''MeharPratap Singh (ProCogia)''' == Milestones == === Achieved === == Open Questions == == Minutes == 6b334b08102d5253843fb98967c9a48322de785a 65 64 2017-01-21T16:03:11Z MeharPratapSingh 15 /* Moving Parts */ wikitext text/x-wiki == Background == We have seen an exponential increase in the demand of R among a large and variant set of audiences. People from various domains are keen to learn it and further improve their skills. This has created a supply and demand gap which is being filled by various teaching channels. While there is no dearth of the amount of R teaching material, both in-class and online, there is still arguably a shortage in skilled R users who possess quality skills in R. This shortage of qualified personnel and abundance of self-taught data scientists leads to confusion for employers as well as prospective employees who have the required skill-set but no way to differentiate themselves. == Proposal == There is no system today to certify qualified R Professionals. The R-Consortium, as the governing body for the R community, needs to step in as the neutral agency before another third-party comes in with a similar certification mechanism and tries to fill this gap. From a competitive perspective, SPSS and SAS already has a certification mechanism in place. == Moving Parts == We understand that there are multiple moving pieces and we have identified 3 main areas to bucket them – 1. Specialization 2. Training 3. Testing 4. Certification We have also taken a stab at the sub categories under those buckets and the multiple paths that we can have for those functional areas that would allow the R Community to solve for the above mentioned challenges. [[File:Initial Path Visual.PNG|thumb|The likely path of certification to be taken in the initial cut]] == Key decisions to be made == The WG will work on the following elements regarding R certification: 1. Financials – * Cost ** Vendor Cost ** Consortium Cost ** Student Cost * Profits 2. Marketing and Promotions 3. Testing * Pass/Fail outcome * Percentage – 0 to 100% * Certification Levels 4. How long the certificate remains valid 5. Partnerships * Training * Testing * Certifications 6. Curriculum – * Generic * Pharmacy * Financial * Visualizations * Others 7. Phased Approach * Identify the next couple of phases * Phase 1 * Phase 2 == Members == * David Smith (Microsoft) * Jeremy Reynolds (Microsoft) * Jonathan Cornelissen (Datacamp) * Martijn Theuwissen (DataCamp) * Mark Sellors (Mango Solution) * Aimee Gott (Mango Solutions) * ''Hadley Wickham (ISC liason, RStudio)'' * Mine Cetinkaya-Rundel (RStudio) * Dinesh Nirmal (IBM) * '''MeharPratap Singh (ProCogia)''' == Milestones == === Achieved === == Open Questions == == Minutes == 9ee17819fe835124061f6225b39f218465da4bc0 64 63 2017-01-21T16:02:41Z MeharPratapSingh 15 /* Members */ wikitext text/x-wiki == Background == We have seen an exponential increase in the demand of R among a large and variant set of audiences. People from various domains are keen to learn it and further improve their skills. This has created a supply and demand gap which is being filled by various teaching channels. While there is no dearth of the amount of R teaching material, both in-class and online, there is still arguably a shortage in skilled R users who possess quality skills in R. This shortage of qualified personnel and abundance of self-taught data scientists leads to confusion for employers as well as prospective employees who have the required skill-set but no way to differentiate themselves. == Proposal == There is no system today to certify qualified R Professionals. The R-Consortium, as the governing body for the R community, needs to step in as the neutral agency before another third-party comes in with a similar certification mechanism and tries to fill this gap. From a competitive perspective, SPSS and SAS already has a certification mechanism in place. == Moving Parts == We understand that there are multiple moving pieces and we have identified 3 main areas to bucket them – 1. Specialization 2. Training 3. Testing 4. Certification We have also taken a stab at the sub categories under those buckets and the multiple paths that we can have for those functional areas that would allow the R Community to solve for the above mentioned challenges. [[File:Initial Path Visual.PNG|thumb|The likely path of certification to be taken in the initial cut]] == Key decisions to be made == The WG will work on the following elements regarding R certification: 1. Financials – * Cost ** Vendor Cost ** Consortium Cost ** Student Cost * Profits 2. Marketing and Promotions 3. Testing * Pass/Fail outcome * Percentage – 0 to 100% * Certification Levels 4. How long the certificate remains valid 5. Partnerships * Training * Testing * Certifications 6. Curriculum – * Generic * Pharmacy * Financial * Visualizations * Others 7. Phased Approach * Identify the next couple of phases * Phase 1 * Phase 2 == Members == * David Smith (Microsoft) * Jeremy Reynolds (Microsoft) * Jonathan Cornelissen (Datacamp) * Martijn Theuwissen (DataCamp) * Mark Sellors (Mango Solution) * Aimee Gott (Mango Solutions) * ''Hadley Wickham (ISC liason, RStudio)'' * Mine Cetinkaya-Rundel (RStudio) * Dinesh Nirmal (IBM) * '''MeharPratap Singh (ProCogia)''' == Milestones == === Achieved === == Open Questions == == Minutes == e30dc668cf9a97afd881966afc8ad1f2caeccd8f 63 62 2017-01-21T16:01:28Z MeharPratapSingh 15 /* Key decisions to be made */ wikitext text/x-wiki == Background == We have seen an exponential increase in the demand of R among a large and variant set of audiences. People from various domains are keen to learn it and further improve their skills. This has created a supply and demand gap which is being filled by various teaching channels. While there is no dearth of the amount of R teaching material, both in-class and online, there is still arguably a shortage in skilled R users who possess quality skills in R. This shortage of qualified personnel and abundance of self-taught data scientists leads to confusion for employers as well as prospective employees who have the required skill-set but no way to differentiate themselves. == Proposal == There is no system today to certify qualified R Professionals. The R-Consortium, as the governing body for the R community, needs to step in as the neutral agency before another third-party comes in with a similar certification mechanism and tries to fill this gap. From a competitive perspective, SPSS and SAS already has a certification mechanism in place. == Moving Parts == We understand that there are multiple moving pieces and we have identified 3 main areas to bucket them – 1. Specialization 2. Training 3. Testing 4. Certification We have also taken a stab at the sub categories under those buckets and the multiple paths that we can have for those functional areas that would allow the R Community to solve for the above mentioned challenges. [[File:Initial Path Visual.PNG|thumb|The likely path of certification to be taken in the initial cut]] == Key decisions to be made == The WG will work on the following elements regarding R certification: 1. Financials – * Cost ** Vendor Cost ** Consortium Cost ** Student Cost * Profits 2. Marketing and Promotions 3. Testing * Pass/Fail outcome * Percentage – 0 to 100% * Certification Levels 4. How long the certificate remains valid 5. Partnerships * Training * Testing * Certifications 6. Curriculum – * Generic * Pharmacy * Financial * Visualizations * Others 7. Phased Approach * Identify the next couple of phases * Phase 1 * Phase 2 == Members == * David Smith (Microsoft) * Jeremy Reynolds (Microsoft) * Jonathan Cornelissen (Datacamp) * Martijn Theuwissen (DataCamp) * Mark Sellors (Mango Solution) * Aimee Gott (Mango Solutions) * Hadley Wickham (ISC liason, RStudio) * Mine Cetinkaya-Rundel (RStudio) * Dinesh Nirmal (IBM) * MeharPratap Singh (ProCogia) == Milestones == === Achieved === == Open Questions == == Minutes == d9e3698ee6cc4eac112c67c1cb7620a727f7de84 62 59 2017-01-21T15:27:38Z MeharPratapSingh 15 wikitext text/x-wiki == Background == We have seen an exponential increase in the demand of R among a large and variant set of audiences. People from various domains are keen to learn it and further improve their skills. This has created a supply and demand gap which is being filled by various teaching channels. While there is no dearth of the amount of R teaching material, both in-class and online, there is still arguably a shortage in skilled R users who possess quality skills in R. This shortage of qualified personnel and abundance of self-taught data scientists leads to confusion for employers as well as prospective employees who have the required skill-set but no way to differentiate themselves. == Proposal == There is no system today to certify qualified R Professionals. The R-Consortium, as the governing body for the R community, needs to step in as the neutral agency before another third-party comes in with a similar certification mechanism and tries to fill this gap. From a competitive perspective, SPSS and SAS already has a certification mechanism in place. == Moving Parts == We understand that there are multiple moving pieces and we have identified 3 main areas to bucket them – 1. Specialization 2. Training 3. Testing 4. Certification We have also taken a stab at the sub categories under those buckets and the multiple paths that we can have for those functional areas that would allow the R Community to solve for the above mentioned challenges. [[File:Initial Path Visual.PNG|thumb|The likely path of certification to be taken in the initial cut]] == Key decisions to be made == The WG will work on the following elements regarding R certification: 1. Financials – o Cost  Vendor Cost  Consortium Cost  Student Cost o Profits 2. Marketing and Promotions 3. Testing o Pass/Fail outcome o Percentage – 0 to 100% o Certification Levels 4. How long the certificate remains valid 5. Partnerships o Training o Testing o Certifications 6. Curriculum – o Generic o Pharmacy o Financial o Visualizations o Etc. 7. Phased Approach o Identify the next couple of phases o Phase 1 o Phase 2 o Etc. == Members == * David Smith (Microsoft) * Jeremy Reynolds (Microsoft) * Jonathan Cornelissen (Datacamp) * Martijn Theuwissen (DataCamp) * Mark Sellors (Mango Solution) * Aimee Gott (Mango Solutions) * Hadley Wickham (ISC liason, RStudio) * Mine Cetinkaya-Rundel (RStudio) * Dinesh Nirmal (IBM) * MeharPratap Singh (ProCogia) == Milestones == === Achieved === == Open Questions == == Minutes == cf945c3c071a289c048a2c94acb28cb6a4189a46 59 2017-01-21T15:12:42Z MeharPratapSingh 15 Created page with "== Background == We have seen an exponential increase in the demand of R among a large and variant set of audiences. People from various domains are keen to learn it and furt..." wikitext text/x-wiki == Background == We have seen an exponential increase in the demand of R among a large and variant set of audiences. People from various domains are keen to learn it and further improve their skills. This has created a supply and demand gap which is being filled by various teaching channels. While there is no dearth of the amount of R teaching material, both in-class and online, there is still arguably a shortage in skilled R users who possess quality skills in R. This shortage of qualified personnel and abundance of self-taught data scientists leads to confusion for employers as well as prospective employees who have the required skill-set but no way to differentiate themselves. == Proposal == There is no system today to certify qualified R Professionals. The R-Consortium, as the governing body for the R community, needs to step in as the neutral agency before another third-party comes in with a similar certification mechanism and tries to fill this gap. From a competitive perspective, SPSS and SAS already has a certification mechanism in place. == Moving Parts == We understand that there are multiple moving pieces and we have identified 3 main areas to bucket them – 1. Specialization 2. Training 3. Testing 4. Certification We have also taken a stab at the sub categories under those buckets and the multiple paths that we can have for those functional areas that would allow the R Community to solve for the above mentioned challenges. [[File:Example.jpg]] == Members == * '''Michael Lawrence''' (Genentech) * '''Indrajit Roy''' (HP Enterprise) * ''Joe Rickert'' (ISC liason, RStudio) * Bernd Bischl (LMU) * Matt Dowle (H2O) * Mario Inchiosa (Microsoft) * Michael Kane (Yale) * Javier Luraschi (RStudio) * Edward Ma (HP Enterprise) * Luke Tierney (University of Iowa) * Simon Urbanek (AT&T) * Bryan Lewis (Paradigm4) * Hossein Falaki (databricks) == Milestones == === Achieved === * Adopt ddR as a prototype for a standard API for distributed computing in R === 2016 Internship === Clark Fitzgerald, a PhD student in the UC Davis Statistics department, worked on ddR and Spark integration. * Wrote [https://github.com/clarkfitzg/sparklite sparklite] and [https://github.com/clarkfitzg/rddlist rddlist] as minimal proof-of-concept R packages to connect and store general data on Spark. [https://docs.google.com/presentation/d/1WfUQ2ockNku90GWMXonEhUEcVOWcgBmWwt5uYSSBYPY/edit?usp=sharing slides] * [https://issues.apache.org/jira/browse/SPARK-16785 Patched SparkR] to allow user defined functions returning binary columns. This allows implementation of different data structures in SparkR. * Updated [https://github.com/vertica/ddR/wiki/Design design documents] with suggested changes to DDR's internal design and object oriented model. * Improved [https://github.com/vertica/ddR/pull/15 testing and ddR internals]. === Outstanding === * Agree on a final standard API for distributed computing in R * Implement at least one scalable backend based on an open-source technology like Spark, SQL, etc == Open Questions == * How can we address the needs of both the end user data scientists and the algorithm implementers? * How should we share data between R and a system like Spark? * Is there any way to unify SparkR and sparklyr? * Could we use the abstractions of tensorflow to partially or fully integrate with platforms like Spark? == Minutes == === 12/08/2016 === * Yuan Tang from Uptake was the presenter ** Michael and Indrajit will write a status report for the working group sometime in December or January ** Yuan gave an overview of TensorFlow ** JJ, Dirk and Yuan are working on R layer for TensorFlow ** TensorFlow is a platform for machine learning as well as other computations (even math proofs). ** It is GPU optimized and distributed. ** It is used in search, speech recognition, Google photos, etc. ** TensorFlow computations are directed graphs. odes are operations and edges are tensors. ** A lot of array, matrix, etc. operations are available ** Backend is mostly C++. Python front end exists. ** TensorFlow R is based on the python fronted ** In multi-device setting, TensorFlow figures out which devices to use and manages communication between devices. ** Computations are fault tolerant ** Yuan has previously worked on Scikit Flow which is now TF.Learn. It’s a easy transition for Scikit learn users. ** Yuan gave a brief overview of the python interface ** TensorFlow in R handles conversion between R and Python. Syntax is very similar to python API ** Future work: Adding more examples and tutorials, integration with Kubernetes/Marathon like framework. ** During the Q/A there were questions related to whether R kernels can be supported in TensorFlow, and whether R dataframes are a natural wrapper for TensorFlow objects. === 11/10/2016 === * SparkR slides were presented by Hossein Faliki and Shivaram from Databricks and UC Berkeley: ** SparkR was a prototype from AMPLab (2014). Initially it had the RDD API and was similar to PySpark API ** In 2015, the merge with upstream Spark, the decision was made to integrate with the Dataframe API, and hide the RDD API ** In 2016 more MLLib algorithms have been integrated and new APIs have been added. A CRAN package will be released soon ** Original SparkR architecture runs R on the master that communicates with the JVM processes in the driver. the driver sends commands to the worker JVM processes, and executes them as scala/java statements. ** The system can read distributed data inside the JVM from different sources such as S3, HDFS, etc. ** The driver has a socket based connection between SparkR and the RBackend. RBackend runs on the JVM, deserializes the R code, and converts the R statements into Java calls. ** collect() and createDataFrame() are used to move data between R and JVM processes. createDataFrame will convert your local R data into a JVM based distributed data frame. ** The API has IO, Caching, MLLib, and SQL related commands ** Since Spark 2.0, we can run R processes inside the JVM worker processes. There is no need to keep long running R processes. ** There are 3 UDF functions (1) lapply, runs function on different value of a list (2) dapply, runs function on each partition of a data frame. You have to careful about how data is partitioned, and (3) gapply, performs a grouping on different column names and then runs the function on each group. ** The new CRAN package install.spark() will automatically download and install Spark. Automated CRAN checks have been added to every commit to the code. Should be available with Spark 2.1.0 * Q/A ** Currently trying to get zero copy dataframe between python and Spark. Spark 2.0 has an off heap manager that uses Arrow. Once this feature is tested on the Python API, the next step will be integration R. ** Spark dataframes gain from plan optimizations. It is not SparkR specific. R UDFs are still treated as black boxes by the optimizer ** Spark doesn't directly support matrixes. There is no immediate intent to do so either. One can store an array or vector as a single column of a Spark dataframe. === 10/13/2016 === ''Detailed minutes were not taken for this meeting'' * Mario Inchiosa: Microsoft's perspective on distributed computing with R ** Microsoft R Server: abstractions and algorithms for distributed computation on top of open-source R ** Desired features of a distributed API like ddR: *** Supports PEMA (initialize, processData, updateResults, processResults) *** Cross-platform *** Fast runtime *** Supports algorithm writer and data scientist *** Comes with a comprehensive set of algorithms *** Easy deployment ** ddR is making good progress but does not yet meet those requirements * Indrajit: ddR progress report and next steps ** Recap of Clark's internship ** Next step: implement some of Clark's design suggestions: https://github.com/vertica/ddR/wiki/Design ** Spark integration will be based on sparklyr ** Should we limit Spark interaction to the DataFrame API or directly interact with RDDs? *** Consensus: will likely need flexibility of RDDs to implement everything we need, e.g., arrays and lists ** Clark and Javier raised concerns about the scalability of sharing data between R and Spark *** Michael: Spark is a platform in its own right, so interoperability is important, should figure something out *** Bryan Lewis: Why not use tensor abstraction from tensorflow? Spark supports tensorflow and an R interface is already in the works. ** Michael raised the issue of additional funding from the R Consortium to continue Clark's work *** Joe Rickert suggested that the working group develop one or more white papers summarizing the findings of the working group for presentation to the Infrastructure Steering Committee. *** Consensus was in favor of this, and several pointed out that the progress so far has been worthwhile, despite not meeting the specific goals laid out in the proposal. * Michael: do we want to invite some external speakers, one per meeting, from groups like databricks, tensorflow, etc? ** Consensus was in favor. === 9/8/2016 === ''Detailed minutes were not taken for this meeting'' * Clark Fitzgerald: internship report ** Developed two packages for low-level Spark integration: rddlist, sparklite ** Patched a bug in Spark ** ddR needs refactoring before Spark integration is feasible: *** dlist, dframe, and darray should be formal classes. *** Partitions of data should be represented by a distributed list abstraction, and most functions (e.g., dmapply) should be implemented on top of that list. * Javier: sparklyr update ** Preparing for CRAN release ** Mario: what happened to sparkapi? *** Javier: sparkapi has been merged into sparklyr in order to avoid overhead of maintaining two packages. ddR can do everything it needs with sparklyr. * Luke Tierney: Update on the low-level vector abstraction, which might support interfaces like ddR and sparklyr. ** Overall approach seems feasible, but still working out a few details. ** Will land in a branch soon. * Bernd Bischl: update on the batchtools package ** Successor to BatchJobs based on in-memory database === 8/11/2016 === ''Meeting was canceled due to lack of availability.'' === 7/14/2016 === * Introduced Clark who is the intern funded by R Consortium. Clark is a graduate student from UC Davis. He will work on ddR integration with Spark and improving the core ddR API as well such as adding a distributed apply() for matrices, split function, etc. * Bernd: Can I play around with ddR now? What backend should I use? How robust is the code? ** Clark: It's in good enough shape to be played around with. We will continue to improve it. Hopefully the spark integration will be done before the end of my internship in September. * Q: Is anyone working on using ddR to make ML scale better. ** Indrajit: We have kmeans, glm, etc. already in CRAN. ** Michael Kane: We are working on glmnet and other packages related to algorithm development. * Javier gave a demo of sparklyr and sparkapi. ** Motivation for the pacakage: The SparkR package overrides the dplyr interface. This is an issue for RStudio. SparkR is not a CRAN package which makes it difficult to add changes. dplyr is the most popular tool by RStudio and is broken on SparkR. ** Sparklyr provides a dplyr interface. It will also support ML like interfaces, such as consuming a ML model. ** Sparklyr does not currently support any distributed computing features. Instead we can recommend ddR as the distributed computing framework on top of sparkapi. We will put the code in CRAN in a couple of weeks. ** Simon: Can you talk more about the wrapper/low level API to work with Spark? *** Javier: The package underneath the cover is called "sparkapi" it is to be used by pacakge builders. "spark_context()" and "invoke()" are the functionality to call scala methods. It does not you to currently run R user defined functions. I am currently working on enabling that feature. Depending upon the interest in using ddR with sparkapi, I can spend more time to make sparkapi feature rich. ** Indrajit: What versions of Spark are supported *** Javier: Anything after 1.6 ** Bernd: How do you export data? *** Javier: We are using all the code from SparkR. So everything in SparkR should continue to work. We don't need to change SparkR. We just need to maintain compatibility. ** Bernd: What happens when the RDDs are very large? *** Javier: Spark will spill on disk. * Michael Kane: Presented examples that he implemented on ddR. ** Talked about how the different distributed packages compare to each other in terms of functionality. ** Michael K. looked at glm and truncated SVD on ddR. Was able to implement irls on ddR by implementing two distributed functions such as "cross". In truncated SVD only needed to overload two different distributed multiplications. ** Ran these algorithms on the 1000 genome dataset. ** Overall liked ddR since it was easy to implement the algorithms in the package. ** New ideas: *** Trying to separate the data layer from the execution layer *** Create an API that works on "chunks" (which is similar to the "parts" API in ddR). Would like to add these APIs to ddR. *** Indrajit: You should be able to get some of the chunk like features by using parts and dmapply. E.g., you can call dmapply to read 10 different files, which correspond 10 chunks now. These are however wrapped as a darray or dframe. But you can continue to work on the individual chunks by using parts(i). === 6/2/2016 === * Round table introduction * (Michael) Goals for the group: ** Make a common abstraction/interfaces to make it easier to work with distributed data and R ** Unify the interface ** Working group will run for a year. Get an API defined, get at least one open source reference implementations ** not everyone needs to work hands on. We will create smaller groups to focus on those aspects. ** We tried to get a diverse group of participants * Logistics: meet monthly, focus groups may meet more often * R Consoritum may be able to figure ways to fund smaller projects that come out of the working group * Michael Kane: Should we start with an inventory of what is available and people are using? ** Michael Lawrence: Yes, we should find the collection of tools as well as the use cases that are common. ** Joe: I will figure out a wiki space. * Javier: Who are the end users? Simon: Common layer needed to get algorithms working. We started from algos and tried to find the minimal common api. One of the goals is to make sure everyone is on the same page and not trying to create his/her own custom interface. * Javier: Should we try to get people with more algo expertise? * Joe: Simon do you have a stack diagram? * Simon: Can we get R Consortium to help write things up and draw things? * Next meeting: Javier is going to present SparkR next time. 499a199feeaa7ec74a4a9d6a653dcb19c63b9c5a 0 15 40 2016-08-19T16:48:50Z Trishan 3 Created page with "== R Consortium and the R Community Code of Conduct == The R Consortium, like the R community as a whole, is made up of members from around the globe with a diverse set of sk..." wikitext text/x-wiki == R Consortium and the R Community Code of Conduct == The R Consortium, like the R community as a whole, is made up of members from around the globe with a diverse set of skills, personalities, and experiences. It is through these differences that our community experiences great successes and continued growth. Members of the R Consortium and their representatives are bound to follow this R Community Code of Conduct (which is based on the Python Community Code of Conduct). We encourage all members of the R community to likewise follow these guidelines which help steer our interactions and strive to keep '''R''' a positive, successful, and growing community. == R Community Code of Conduct== A member of the R Community is: '''Open:''' Members of the community are open to collaboration, whether it's on projects, working groups, packages, problems, or otherwise. We're receptive to constructive comment and criticism, as the experiences and skill sets of other members contribute to the whole of our efforts. We're accepting of anyone who wishes to take part in our activities, fostering an environment where all can participate and everyone can make a difference. '''Considerate:''' Members of the community are considerate of their peers — other R users. We're thoughtful when addressing the efforts of others, keeping in mind that oftentimes the labor was completed simply for the good of the community. We're attentive in our communications, whether in person or online, and we're tactful when approaching differing views. '''Respectful:''' Members of the community are respectful. We're respectful of others, their positions, their skills, their commitments, and their efforts. We're respectful of the volunteer efforts that permeate the R community. We're respectful of the processes set forth in the community, and we work within them. When we disagree, we are courteous in raising our issues. Overall, we're good to each other. We contribute to this community not because we have to, but because we want to. If we remember that, these guidelines will come naturally. '''Questions/comments/reports?''' Please write to the Code of Conduct address: ''conduct@r-consortium.org''. (this will email the Board Chair and R Consortium Program manager). Include any available relevant information, including links to any publicly accessible material relating to the matter. b33148b94e10e5b594e5766d20cdc678c129ebfd 0 6 35 33 2016-07-03T01:35:56Z Lukasstadler 8 wikitext text/x-wiki = Working Group: Future-proof Native APIs for R = This working groups will assess current native API usage, gather community input, and work towards an easy-to-understand, consistent and verifiable API that will drive R language adoption. === 2016-06-30 Meeting UseR! === see [[R Native API meeting 2016-06-30]] === 2016-06-20 Teleconference === see [[R Native API call 2016-06-20]] === 2016-06-20 Survey of API usage === see [[Initial Survey of API Usage]] === 2016-06-13 Initial WG members === * Alexander Bertram, BeDataDriven * Torsten Hothorn, University of Zurich * Mick Jordan, Oracle Labs * Stephen Kaluzny, TIBCO (ISC representative) * Michael Lawrence, Genentech * Karl Millar, Google * Duncan Murdoch, University of Western Ontario * Radford Neal, University of Toronto * Edzer Pebesma, University of Münster * Indrajit Roy, HP Labs * Michael Sannella, TIBCO * Lukas Stadler, Oracle Labs * Luke Tierney, University of Iowa * Simon Urbanek, AT&T Research Labs * Jan Vitek, Northeastern University * Gregory Warnes, Boehringer Ingelheim === 2016-05-10 Initial phone meeting === Attendence: * Stephen Kaluzny (WG sponsor in ISC) * Lukas Stadler, Adam Welc, Mark Hornick (authors of the ISC project proposal) Topics: * Defining a WG leader (Lukas Stadler) * Channels on which do distribute call for participation: :* r-devel mailing list :* Authors of important packages (top packages in download stats) with native components :* Alterative and modified implementations: Tibco TERR, renjin, pqr, cxxr/rho, FastR :* Program committee of the RIOT workshop [[http://riotworkshop.github.io]] :* Lukas Stadler and Mick Jordan as initial members from Oracle Labs * Discussion about stopping criteria for the WG / "What is the job of this WG?" * Plan for meetings and discussions, general openness of all WG communication 97ad5e3c46d0b43d63993e5fbcfb381f2954b2cf 33 23 2016-06-25T18:34:37Z Lukasstadler 8 wikitext text/x-wiki = Working Group: Future-proof Native APIs for R = This working groups will assess current native API usage, gather community input, and work towards an easy-to-understand, consistent and verifiable API that will drive R language adoption. === 2016-06-20 Teleconference === see [[R Native API call 2016-06-20]] === 2016-06-20 Survey of API usage === see [[Initial Survey of API Usage]] === 2016-06-13 Initial WG members === * Alexander Bertram, BeDataDriven * Torsten Hothorn, University of Zurich * Mick Jordan, Oracle Labs * Stephen Kaluzny, TIBCO (ISC representative) * Michael Lawrence, Genentech * Karl Millar, Google * Duncan Murdoch, University of Western Ontario * Radford Neal, University of Toronto * Edzer Pebesma, University of Münster * Indrajit Roy, HP Labs * Michael Sannella, TIBCO * Lukas Stadler, Oracle Labs * Luke Tierney, University of Iowa * Simon Urbanek, AT&T Research Labs * Jan Vitek, Northeastern University * Gregory Warnes, Boehringer Ingelheim === 2016-05-10 Initial phone meeting === Attendence: * Stephen Kaluzny (WG sponsor in ISC) * Lukas Stadler, Adam Welc, Mark Hornick (authors of the ISC project proposal) Topics: * Defining a WG leader (Lukas Stadler) * Channels on which do distribute call for participation: :* r-devel mailing list :* Authors of important packages (top packages in download stats) with native components :* Alterative and modified implementations: Tibco TERR, renjin, pqr, cxxr/rho, FastR :* Program committee of the RIOT workshop [[http://riotworkshop.github.io]] :* Lukas Stadler and Mick Jordan as initial members from Oracle Labs * Discussion about stopping criteria for the WG / "What is the job of this WG?" * Plan for meetings and discussions, general openness of all WG communication 70da52e45dcdb50abfb312fdc1d4f5e49cd6a113 23 21 2016-06-23T15:22:22Z Skaluzny 7 /* 2016-06-13 Initial WG members */ wikitext text/x-wiki = Working Group: Future-proof Native APIs for R = === 2016-06-20 Survey of API usage === see [[Initial Survey of API Usage]] === 2016-06-13 Initial WG members === * Alexander Bertram, BeDataDriven * Torsten Hothorn, University of Zurich * Mick Jordan, Oracle Labs * Stephen Kaluzny, TIBCO (ISC representative) * Michael Lawrence, Genentech * Karl Millar, Google * Duncan Murdoch, University of Western Ontario * Radford Neal, University of Toronto * Edzer Pebesma, University of Münster * Indrajit Roy, HP Labs * Michael Sannella, TIBCO * Lukas Stadler, Oracle Labs * Luke Tierney, University of Iowa * Simon Urbanek, AT&T Research Labs * Jan Vitek, Northeastern University * Gregory Warnes, Boehringer Ingelheim === 2016-05-10 Initial phone meeting === Attendence: * Stephen Kaluzny (WG sponsor in ISC) * Lukas Stadler, Adam Welc, Mark Hornick (authors of the ISC project proposal) Topics: * Defining a WG leader (Lukas Stadler) * Channels on which do distribute call for participation: :* r-devel mailing list :* Authors of important packages (top packages in download stats) with native components :* Alterative and modified implementations: Tibco TERR, renjin, pqr, cxxr/rho, FastR :* Program committee of the RIOT workshop [[http://riotworkshop.github.io]] :* Lukas Stadler and Mick Jordan as initial members from Oracle Labs * Discussion about stopping criteria for the WG / "What is the job of this WG?" * Plan for meetings and discussions, general openness of all WG communication 74bcda4359ece2d9ddacd4c2bd048e90b2402cc3 21 20 2016-06-20T15:22:24Z Lukasstadler 8 reversed order wikitext text/x-wiki = Working Group: Future-proof Native APIs for R = === 2016-06-20 Survey of API usage === see [[Initial Survey of API Usage]] === 2016-06-13 Initial WG members === * Alexander Bertram, BeDataDriven * Torsten Hothorn, University of Zurich * Mick Jordan, Oracle Labs * Michael Lawrence, Genentech * Karl Millar, Google * Duncan Murdoch, University of Western Ontario * Radford Neal, University of Toronto * Edzer Pebesma, University of Münster * Indrajit Roy, HP Labs * Michael Sannella, TIBCO * Lukas Stadler, Oracle Labs * Luke Tierney, University of Iowa * Simon Urbanek, AT&T Research Labs * Jan Vitek, Northeastern University * Gregory Warnes, Boehringer Ingelheim === 2016-05-10 Initial phone meeting === Attendence: * Stephen Kaluzny (WG sponsor in ISC) * Lukas Stadler, Adam Welc, Mark Hornick (authors of the ISC project proposal) Topics: * Defining a WG leader (Lukas Stadler) * Channels on which do distribute call for participation: :* r-devel mailing list :* Authors of important packages (top packages in download stats) with native components :* Alterative and modified implementations: Tibco TERR, renjin, pqr, cxxr/rho, FastR :* Program committee of the RIOT workshop [[http://riotworkshop.github.io]] :* Lukas Stadler and Mick Jordan as initial members from Oracle Labs * Discussion about stopping criteria for the WG / "What is the job of this WG?" * Plan for meetings and discussions, general openness of all WG communication 832d0e33d033a5f9a01eccbf9c55a08f2232dff8 20 13 2016-06-20T15:21:44Z Lukasstadler 8 wikitext text/x-wiki = Working Group: Future-proof Native APIs for R = === 2016-05-10 Initial phone meeting === Attendence: * Stephen Kaluzny (WG sponsor in ISC) * Lukas Stadler, Adam Welc, Mark Hornick (authors of the ISC project proposal) Topics: * Defining a WG leader (Lukas Stadler) * Channels on which do distribute call for participation: :* r-devel mailing list :* Authors of important packages (top packages in download stats) with native components :* Alterative and modified implementations: Tibco TERR, renjin, pqr, cxxr/rho, FastR :* Program committee of the RIOT workshop [[http://riotworkshop.github.io]] :* Lukas Stadler and Mick Jordan as initial members from Oracle Labs * Discussion about stopping criteria for the WG / "What is the job of this WG?" * Plan for meetings and discussions, general openness of all WG communication === 2016-06-13 Initial WG members === * Alexander Bertram, BeDataDriven * Torsten Hothorn, University of Zurich * Mick Jordan, Oracle Labs * Michael Lawrence, Genentech * Karl Millar, Google * Duncan Murdoch, University of Western Ontario * Radford Neal, University of Toronto * Edzer Pebesma, University of Münster * Indrajit Roy, HP Labs * Michael Sannella, TIBCO * Lukas Stadler, Oracle Labs * Luke Tierney, University of Iowa * Simon Urbanek, AT&T Research Labs * Jan Vitek, Northeastern University * Gregory Warnes, Boehringer Ingelheim === 2016-06-20 Survey of API usage === see [[Initial Survey of API Usage]] 1f3f5611985e067cfef14250482f63cd0a7e4bd2 13 12 2016-06-13T09:04:59Z Lukasstadler 8 wikitext text/x-wiki = Working Group: Future-proof Native APIs for R = * Initial phone meeting: 2016-05-10 === Initial WG members === * Alexander Bertram, BeDataDriven * Torsten Hothorn, University of Zurich * Mick Jordan, Oracle Labs * Michael Lawrence, Genentech * Karl Millar, Google * Duncan Murdoch, University of Western Ontario * Radford Neal, University of Toronto * Edzer Pebesma, University of Münster * Indrajit Roy, HP Labs * Michael Sannella, TIBCO * Lukas Stadler, Oracle Labs * Luke Tierney, University of Iowa * Simon Urbanek, AT&T Research Labs * Jan Vitek, Northeastern University * Gregory Warnes, Boehringer Ingelheim c82fd7c1241c464d103deac4800b0d6f727c2020 12 2016-05-20T16:20:28Z Skaluzny 7 Created page for the working group wikitext text/x-wiki = Working Group: Future-proof Native APIs for R = * Initial phone meeting: 2016-05-10 4eadb248b047c512a3312e2bd3b93a67b2d98a81 0 13 32 2016-06-25T18:34:21Z Lukasstadler 8 Created page with "An initial meeting intended to provide an opportunity to get an overview of everybody's motivation and view on the matter. ==== A round of introductions ==== *Short introduct..." wikitext text/x-wiki An initial meeting intended to provide an opportunity to get an overview of everybody's motivation and view on the matter. ==== A round of introductions ==== *Short introduction by Stephen Kaluzny (TIBCO, R-consortium ISC member, sponsor for this WG) *Lukas Stadler (Oracle Labs, FastR project, WG lead) :*Is there consensus that the R native API could use an overhaul, more documentation, etc.? :*Some basic points to consider: mall changes vs. big changes, C/C++, separation into different modules :*[[Initial Survey of API Usage]] - some discussions about the % of API needed by most packages and applications - 10%, 80%, 90%? *Simon Urbanek (AT&T Labs) :*Experience from the Aleph project :*You can get a long way with a small portion of the API *Luke Tierney (University of Iowa) :*Has to take care of this if it gets into GNUR :*Generally interested, implements optimizations in R core *Alexander Bertram (BeDataDriven, renjin) :*A lot of the API is just BLAS, etc. - which parts are R specific, how big is the actual interface? :*APIs should also provide guidance to package developers :*R core already moved some code out (graphics) which is useful and improves quality and separation *Radford Neal (University of Toronto) :*PQR, which naturally sees less reason for drastic change :*There's two sides: R->C and C->R ::*.C/.Fortran is IMO the preferred way: small surface, implementation performance can be improved, can, e.g., run in parallel in PQR ::*.Call/.External/...: not very well defined, PROTECT is hard to get right, how to use NAMED (or whatever will replace NAMED) ::*What about the embedded R interface ::*The interface between base R and the included packages (stats, …) should also be well-defined ::*The interface contains much more than just header files, .e.g, config files, databases, ... ::*Discussion: Alex, Simon, Lukas, … :::*data.table - very unique dependency on how internals of GNUR work :::*Validity of .Call functions that modify their arguments without checking NAMED, many uses of this only work given a very specific behavior of the R runtime *Mick Jordan (Oracle Labs, FastR) :*The current R API is an interface to a GNUR-like system, and not to an R runtime :*A small number of function impls have gotten FastR a long way, most tricky parts are in the "callbacks" (the C->R part) :*Missing documentation is a problem, makes it hard to implement API ::*(Lukas) Documentation is also a contract that defines what can be expected, which behavior can be depended upon. Otherwise, users will assume that all observed behavior is part of the API. :*There should be one header file, with documentation :*Java was in a similar position with the first native API, JNI is the result of that, very well-defined interface that stood the test of time *Gregory Warnes (Boehringer Ingelheim) :*RPy, provides a Python interface, generally interested in R native APIs *Edzer Pebesma (University of Münster) :*Looking into roh project, extensions :*Interested to see how R is used in the greater world outside GNUR *Michael Sannella (TIBCO, TERR, owner of the R-C-level API) :*Naturally interested in this :*data.table as an extreme case in native API usage: ::*Managed to get it to work on TERR (talk about this at RIOT [[http://riotworkshop.github.io]]) ::*There are no contracts (in the form of documentation, assertions, ...) in the API, data.table uses this to the extreme ::*It exports some of these extreme uses (e.g., changing attributes) to its users ::*A very interesting/challenging case: it’s a very important package, how to handle this? Make all "API" it uses available? :*IMO, the real problem are that the functions are not well-defined enough, not that there are too many: "whatever you can get away with is defined" :*Few package authors use the API to the max, the average package author has probably not delved so deep into the interface, not used all of it, because lacking documentation is a barrier *Michael Lawrence (Genentech) :*Trying to enable package-level (C-level) extensibility of base R packages :*E.g., new int-vector implementation as a package :*(Alex) Pushed for this in renjin, e.g., provide new implementation of int vectors :*Some more discussion: ::*This would clearly not be possible with the current API because of things like “REAL” ::*Gabriel Becker (works with Michael Lawrence) talks at DSC about a modified GNUR ::*(Alex) renjin has DBI-compatible package that simulates a data.frame from a rolling cursor in a DB ::*Should this be “exchange int vector impl in runtime” (for all int vectors) or “create int vector with this implementation” (operator overloading?) ::*(Mick) R is a very complex system that allows modifying many basic assumptions, should there be more or less complexity? *Indrajit Roy (HP labs) :*Extending R with distributed data structures :*Making API compatible with what people write in the future :*Questions, playing the “bad guy”: ::*Many here want to make changes to the R internal, to the packages, so that R can be run by alternative implementations ::*A lot of the points - are they about coding practices? or about making R internal code more modular? ::*Maybe we just need to deprecate all the unused functions? What’s the real goal? ::*(Alex) It’s not so much about being able to implement it, it works already, but to make it easier, more efficient, etc. ::*(Mick) There should be an unbiased party in this, with a view not only from R core ::*(Simon) A lot of the discussions on r-devel are about what is the API and what not :::*A couple independent views: ::::*Documentation, what is the contract of the API? ::::*People have been using internal API and calling for it to be made external ::::*High-level stuff: replacing high-level pieces ::::*How to make things more flexible ::::*The call is a lot about what people think about the API ==== Additional discussions ==== *Is there someone in this call from the rho project? :*Karl Millar is listening on the mailing list :*They, e.g., did GC with stack scanning to avoid the need for PROTECT :*Discussion about whether the API should include GC aspects *(Lukas) Why is a large part of the interface duplicated on both the R and C side? :*The interface could be a lot smaller if eval(...) was used in all cases where there's not performance bottleneck (connection functions, etc.) :*Functions like “as.vector(…)” and R_asVector: sometimes mismatch between R and C version, sometimes similar :*(Simon) Historical reasons, stems from the R API being taken from the implementation (which is very powerful, but dangerous) Additional (in-person) discussions will be scheduled for useR! and RIOT ==== Wrap-up ==== (Lukas): One important questions to answer for this WG is: How far do we go *Enhance/add documentation *Trimm down the interface (by looking at it's current usage, by looking at what makes sense as an API) *Extend by replacing tricky parts, with a gradual switchover *Introducing a consistent API, with breaking changes *Introduce new APIs for parts that are not covered at the moment (or: include provisions for adding new API in the future) The big tradeoff is between payoff for GNUR and alternative implementations (more efficient, easier to maintain,…), and increasing effort (and less adoption) on the package side. 52deca65ba2ee04947d3bef89dce04349b7095af 0 14 36 34 2016-07-03T01:41:26Z Lukasstadler 8 wikitext text/x-wiki Informal meeting after the end of the useR! 2016 conference. Participants: Michael Sannella, Torsten Hothorn, Dirk Eddelbuettel, Karl Millar, Simon Urbanek, Mick Jordan, Lukas Stadler Discussion topics: * (Dirk) From the POV of Rcpp, lots of useful functionality is hidden and not part of the official API. It hasn't changed in a long time, why not make available? :* It's not uncommon that people copy out code to make it available. :* comment on data.table: it has a tiny dependency trail, and keeps working with very old R versions. * (Torsten) Packages like stats do not export their functionality at the native level (or there are problems with dependency resolution). :* Another case where people start copying out code. :* Is it possible to get symbols from specific package? yes... * "eval" could be much more efficient if it had a "prepare" and an "execute" step, like prepared DB statements. :* Combined with a concise API, this would allow much more R functions to be reused on the native side, without a need for explicit C API. ::* Or have simple C wrappers, which can be replaced with a direct implementation in case of performance problems. ::* Do connection functions, e.g., have to be efficient? ::* Makes for good documentation - "behaves like as.integer" (maybe "sans S3/S4 dispatch") * Is it "future proofing the API" or "future proofing packages"? * Discussions related to CRAN: :* Abandoned but popular packages sometimes get fixed by CRAN maintainers. :* How could a larger set of changes produced by API renamings be handled? ::* Hard in the current system... ::* Having "master" versions of all packages on github would help. ::* Licensing / openness concerns with github? :* Testing of GNUR with modified API? ::* Many packages require additional steps, installed libraries, etc. ::* Maybe r-hub could help? (Lukas will contact Gabor Csardi) ::* Two levels where changes can cause packages to fail: installing (compiling) and testing (where examples exist) * What's the reason for the different prefixes? :* Rf_..., R..., or no prefix, camel case, upper case, underscores, etc. :* Historical reasons - cleanup could be done with tools or sed scripts. * USE_RINTERNALS does two things: additional functionality and better performance :* the former could be achieved by different include files :* the latter should not be necessary (why not have everything at top speed, but leave the API in a state that can be verified?) :* it should be possible to create a wrapper around the API that checks the (documented) contract as tightly as possible * The manual still explains functionality that is generally considered to be wrong (e.g., "TYPE(x) = LANGSXP;") * There should be no global variables, only functions (or at least a contract that allows them to be implemented as functions) :* Not only CRAN - we need to describe the universe of (important?) packages. :* Dependencies between functions? (sic!) * General steps this WG should/could take: :* Tighten API - remove stuff that is not used ::* Remove altogether, or deprecate (or hide behind a #define USE_DEPRECATED_API) :* Renaming functions? ::* Maybe we want to introduce a new naming scheme? ::* Maybe have a period with both naming schemes :* Document the functions ::* Describe the arguments and its contract. ::* Who could do that? For some functions only core R developers can give a real account of their intended contract. ::* Some functions are tightly related to R functions - maybe describe them in relation to these? :* Breaking packages is ok, to a certain degree :* You could do a lot via eval if the details of its behavior were defined well and non-surprising ::* Getting proper error context at the C level? ::* Java solved this with the Java Virtual Machine Tooling API (JVMTI) :* Maybe create shims of R functions as a new API? docs? * Immediate next step: :* (Lukas) Define the "tighten API" task, what it entails, as a (student?) project, and find a "volunteer" 12106a74b94a46a9059e2e9f9889d11d6c832035 34 2016-07-03T01:35:52Z Lukasstadler 8 Created page with "Informal meeting after the end of the useR! 2016 conference. Participants: Michael Sannella, Torsten Hothorn, Dirk Eddelbuettel, Karl Millar, Simon Urbanek, Mick Jordan, Luka..." wikitext text/x-wiki Informal meeting after the end of the useR! 2016 conference. Participants: Michael Sannella, Torsten Hothorn, Dirk Eddelbuettel, Karl Millar, Simon Urbanek, Mick Jordan, Lukas Stadler Incomplete account of discussion topics: * (Dirk) From the POV of Rcpp, lots of useful functionality is hidden and not part of the official API. It hasn't changed in a long time, why not make available? :* It's not uncommon that people copy out code to make it available. :* comment on data.table: it has a tiny dependency trail, and keeps working with very old R versions. * (Torsten) Packages like stats do not export their functionality at the native level (or there are problems with dependency resolution). :* Another case where people start copying out code. :* Is it possible to get symbols from specific package? yes... * "eval" could be much more efficient if it had a "prepare" and an "execute" step, like prepared DB statements. :* Combined with a concise API, this would allow much more R functions to be reused on the native side, without a need for explicit C API. ::* Or have simple C wrappers, which can be replaced with a direct implementation in case of performance problems. ::* Do connection functions, e.g., have to be efficient? ::* Makes for good documentation - "behaves like as.integer" (maybe "sans S3/S4 dispatch") * Is it "future proofing the API" or "future proofing packages"? * Discussions related to CRAN: :* Abandoned but popular packages sometimes get fixed by CRAN maintainers. :* How could a larger set of changes produced by API renamings be handled? ::* Hard in the current system... ::* Having "master" versions of all packages on github would help. ::* Licensing / openness concerns with github? :* Testing of GNUR with modified API? ::* Many packages require additional steps, installed libraries, etc. ::* Maybe r-hub could help? (Lukas will contact Gabor Csardi) ::* Two levels where changes can cause packages to fail: installing (compiling) and testing (where examples exist) * What's the reason for the different prefixes? :* Rf_..., R..., or no prefix, camel case, upper case, underscores, etc. :* Historical reasons - cleanup could be done with tools or sed scripts. * USE_RINTERNALS does two things: additional functionality and better performance :* the former could be achieved by different include files :* the latter should not be necessary (why not have everything at top speed, but leave the API in a state that can be verified?) :* it should be possible to create a wrapper around the API that checks the (documented) contract as tightly as possible * The manual still explains functionality that is generally considered to be wrong (e.g., "TYPE(x) = LANGSXP;") * There should be no global variables, only functions (or at least a contract that allows them to be implemented as functions) :* Not only CRAN - we need to describe the universe of (important?) packages. :* Dependencies between functions? (sic!) * General steps this WG should/could take: :* Tighten API - remove stuff that is not used ::* Remove altogether, or deprecate (or hide behind a #define USE_DEPRECATED_API) :* Renaming functions? ::* Maybe we want to introduce a new naming scheme? ::* Maybe have a period with both naming schemes :* Document the functions ::* Describe the arguments and its contract. ::* Who could do that? For some functions only core R developers can give a real account of their intended contract. ::* Some functions are tightly related to R functions - maybe describe them in relation to these? :* Breaking packages is ok, to a certain degree :* You could do a lot via eval if the details of its behavior were defined well and non-surprising ::* Getting proper error context at the C level? ::* Java solved this with the Java Virtual Machine Tooling API (JVMTI) :* Maybe create shims of R functions as a new API? docs? * Immediate next step: :* (Lukas) Define the "tighten API" task, what it entails, as a (student?) project, and find a "volunteer" 386054720ef2bf5bd554619cf990d906cec66b83 0 22 83 2017-11-28T17:27:24Z Jmertic 19 Created page with "== Top-level Projects == ''Approved by R Consortium TSC on 2017-10-18'' This document outlines a proposed process by which an ISC project might graduate to top-level project,..." wikitext text/x-wiki == Top-level Projects == ''Approved by R Consortium TSC on 2017-10-18'' This document outlines a proposed process by which an ISC project might graduate to top-level project, as well as the process by which such a project may be terminated. A top-level project implies long-term support (with 3 year review) by the R Consortium for the project, regardless of the person running it. === Context === We currently have three top-level projecst: * R-hub, by Gabor Csardi. * RUGS program (incl. small conferences) * R-Ladies Top-level projects imply long term support, and give the project a seat on the ISC. === Project Promotion=== Generally, we will consider the following factors when deciding if a project should become a top-level project: # The project is important, and is having a significant impact on the R community. # The project has completed one year of successful funding, and delivered their first annual report. # Commitment (to some extent) independently of the personnel on initial project. Project would be nominated by ISC member, and confirmed by a simple majority vote. Then ISC chair would reach out to project and discuss budget etc === Budget=== The project would prepare a rough 3 year plan, including discussion of personnel (i.e. either a commitment from the original grantee or a transition plan). Top level project would be allotted a line item on the ISC budget to ensure priority funding. Upon graduating, any remaining funding from initial grant will return to ISC. === Reporting=== The project would then be expected to have a status report at every regular meeting of the ISC to be presented by their ISC representative. In lieu of regular project proposals, top-level projects would submit a proposed yearly budget by October 31 (in order to get budgeted for following financial year). This would serve as a regular review point for top-level projects, and would occur in a separate meeting to the other project proposals, with the ISC member associated with the project recusing themselves. === Concluding support=== Top-level projects will be reviewed every three years. An explicit positive vote would be required to continue funding. In exceptional circumstances, a top-level project may be terminated at any time by a simple majority vote of the ISC. A top-level project will typically not be terminated if the grantee resigns, provided that succession plan is in place. 754125936562d03785cd7f8b817881eae3d90414 6 21 79 2017-05-12T02:52:32Z Indrajit roy 14 User created page with UploadWizard wikitext text/x-wiki =={{int:filedesc}}== {{Information |description={{en|1=Futures in R}} |date=2017-05-11 19:51:03 |source=Henrik Bengtsson |author=Henrik Bengtsson |permission= |other_versions= }} =={{int:license-header}}== {{subst:uwl}} [[Category:Uploaded with UploadWizard]] ce087f26ec85e4e61124e35b6875799d76d60fab 6 17 60 2017-01-21T15:16:20Z MeharPratapSingh 15 User created page with UploadWizard wikitext text/x-wiki =={{int:filedesc}}== {{Information |description={{en|1=The likely path of certification to be taken in the initial cut}} |date=2017-01-21 07:14:51 |source={{own}} |author=[[User:MeharPratapSingh|MeharPratapSingh]] |permission= |other_versions= }} =={{int:license-header}}== {{self|cc-by-sa-3.0}} [[Category:Uploaded with UploadWizard]] 7ee32c4fed62023699eb5e88fbf4dd963dddbc40 6 18 61 2017-01-21T15:16:20Z MeharPratapSingh 15 User created page with UploadWizard wikitext text/x-wiki =={{int:filedesc}}== {{Information |description={{en|1=Multiple options currently available for R Certification}} |date=2017-01-21 07:14:51 |source={{own}} |author=[[User:MeharPratapSingh|MeharPratapSingh]] |permission= |other_versions= }} =={{int:license-header}}== {{self|cc-by-sa-3.0}} [[Category:Uploaded with UploadWizard]] cb0b719b7ab2a660d1de7be113652318de878fad 6 5 10 2016-05-18T18:14:57Z Jfarwell 6 User created page with UploadWizard wikitext text/x-wiki =={{int:filedesc}}== {{Information |description={{en|1=test2}} |date=2016-05-18 11:14:44 |source={{own}} |author=[[User:Jfarwell|Jfarwell]] |permission= |other_versions= }} =={{int:license-header}}== {{self|cc-by-sa-3.0}} [[Category:Uploaded with UploadWizard]] 06d2225203b57b3af0be90faf0c59a7dfe20f6cd 6 4 9 2016-05-18T18:14:56Z Jfarwell 6 User created page with UploadWizard wikitext text/x-wiki =={{int:filedesc}}== {{Information |description={{en|1=test1}} |date=2016-05-18 11:14:38 |source={{own}} |author=[[User:Jfarwell|Jfarwell]] |permission= |other_versions= }} =={{int:license-header}}== {{self|cc-by-sa-3.0}} [[Category:Uploaded with UploadWizard]] 401e039cebd18967e10474579d9cbc68a27b025c 8 2 2 2016-03-05T01:15:44Z Lfadmin 1 Created page with "Log in / Register with Linux Foundation ID" wikitext text/x-wiki Log in / Register with Linux Foundation ID 63fda8e353ae27d0843ffc3a16f2248f04300f97